L. Hoai-an, X. T. Thi, and . Vo, Tao Pham Dinh. DC programming and DCA for nonnegative matrix factorization, ICCCI 2014: Proceedings of 6th International Conference on Computational Collective Intelligence Technologies and Applications, LNAI 8733, pp.573-582, 2014.

L. Hoai-an, X. T. Thi, and . Vo, Tao Pham Dinh. Efficient Nonegative Matrix Factorization via DC programming and DCA. Revised version to Neural Computation

X. T. Vo, H. A. , L. Thi, T. Pham-dinh, T. B. et al., DC Programming and DCA for Dictionary Learning, Eds): ICCCI 2015, Part I, LNAI 9329, pp.295-304, 2015.
DOI : 10.1109/TSP.2010.2040671

X. T. Vo, H. A. , and L. Thi, DC Programming and DCA for Dictionary Learning and Application in Image Denoising

L. Hoai-an, X. T. Thi, T. Vo, and . Pham-dinh, Robust Feature Selection for SVMs under Uncertain Data, ICDM 2013: Proceedings of the 13th Industrial Conference on Advances in Data Mining, LNAI 7987, pp.151-165, 2013.

L. Hoai-an, X. T. Thi, T. Vo, and . Pham-dinh, Feature Selection for SVMs under Uncertain Data: Robust optimization based on Difference of Convex functions Algorithms, Neural Networks, vol.59, pp.36-50, 2014.

X. T. Vo, H. A. , and L. Thi, Robust Optimization for Clustering Uncertain Data

X. T. Vo, H. A. , L. Thi, T. Pham-dinh, and P. ?1, Robust Optimization for Clustering Submitted to ACIIDS 2016: 8th Asian Conference on Intelligent Information and Database Systems. Conclusion A.2 Orthogonal matrix Définition A.2.1 A matrix P is said to be orthogonal if it is nonsingular and, The transpose and the inverse of an orthogonal matrix are orthogonal, 2004.

A. Lemma, 2 Suppose that x and y are two n th -order, nonzeros, real, normal column vectors. Then there exists a real orthogonal matrix P such that y = P x

M. Aharon, M. Elad, and A. Bruckstein, $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

D. Aloise, A. Deshpande, P. Hansen, and P. Popat, NP-hardness of Euclidean sum-of-squares clustering, Machine Learning, vol.27, issue.2, pp.245-248, 2008.
DOI : 10.1007/s10994-009-5103-0

E. Amaldi and V. Kann, On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems, Theoretical Computer Science, vol.209, issue.1-2, pp.237-260, 1998.
DOI : 10.1016/S0304-3975(97)00115-1

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with Sparsity-Inducing Penalties, Foundations and Trends?? in Machine Learning, vol.4, issue.1, pp.1-106, 2011.
DOI : 10.1561/2200000015

URL : https://hal.archives-ouvertes.fr/hal-00613125

W. Bajwa, J. Haupt, A. Sayeed, and R. Nowak, Compressive wireless sensing, Proceedings of Fifth Int. Conf. on Information Processing in Sensor Networks, pp.134-142, 2006.

A. M. Bagirov and J. Yearwood, A new nonsmooth optimization algorithm for minimum sum-of-squares clustering problems, European Journal of Operational Research, vol.170, issue.2, pp.578-596, 2006.
DOI : 10.1016/j.ejor.2004.06.014

D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk, Distributed compressed sensing, 2009.

K. P. Bennett and O. L. Mangasarian, Robust linear programming discrimination of two linearly inseparable sets, Optimization Methods and Software, vol.1, issue.1, pp.23-34, 1992.
DOI : 10.1080/10556789208805504

A. Ben-tal, L. Ghaoui, and A. Nemirovski, Robust Optimization, 2009.
DOI : 10.1515/9781400831050

P. Berkhin, A Survey of Clustering Data Mining Techniques. Grouping Multidimensional Data, pp.25-71, 2006.

M. Berry, M. Browne, A. Langville, P. Pauca, and R. Plemmons, Algorithms and applications for approximate nonnegative matrix factorization, Computational Statistics & Data Analysis, vol.52, issue.1, pp.155-173, 2006.
DOI : 10.1016/j.csda.2006.11.006

C. Bhattacharyya, L. R. Grate, M. I. Jordan, L. Ghaoui, and M. I. , Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data, Journal of Computational Biology, vol.11, issue.6, pp.11-1073, 2004.
DOI : 10.1089/cmb.2004.11.1073

C. Bhattacharyya, K. S. Pannagadatta, and A. J. Smola, A second order cone programming formulation for classifying missing data, Advances in Neural Information Processing Systems, 2004.

J. Bi and T. Zhang, Support vector classification with input data uncertainty, Advances in Neural Information Processing Systems (NIPS17), 2004.

P. S. Bradley, O. L. Mangasarian, and W. N. Street, Clustering via concave minimization in, NIPS 9, pp.368-374, 1997.

P. S. Bradley and O. L. Mangasarian, Feature Selection via concave minimization and support vector machines, Proceeding of International Conference on Machina Learning ICML'98, 1998.

P. S. Bradley, O. L. Mangasarian, and J. B. And-rosen, Parsimonious Least Norm Approximation, Computational Optimization and Applications, vol.11, issue.1, pp.5-21, 1998.
DOI : 10.1023/A:1018361916442

M. J. Brusco, A Repetitive Branch-and-Bound Procedure for Minimum Within-Cluster Sums of Squares Partitioning, Psychometrika, vol.10, issue.2, pp.347-363, 2006.
DOI : 10.1177/014662168601000402

D. Cai, X. He, X. Wu, and H. J. , Nonnegative matrix factorization on manifold, Proc. IEEE Int. Conf. Data Mining, pp.63-72, 2008.

E. J. Candes and P. Randall, Highly Robust Error Correction byConvex Programming, IEEE Transactions on Information Theory, vol.54, issue.7, pp.2829-2840, 2006.
DOI : 10.1109/TIT.2008.924688

E. J. Candes and T. Tao, Decoding by Linear Programming, IEEE Transactions on Information Theory, vol.51, issue.12, pp.51-4203, 2005.
DOI : 10.1109/TIT.2005.858979

URL : https://authors.library.caltech.edu/1378/1/CANieeetit05.pdf

E. J. Candes, M. Wakin, and S. Boyd, Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.877-905, 2008.
DOI : 10.1007/978-1-4757-4182-7

C. Caramanis, S. Mannor, and H. Xu, Optimization for Machine Learning. in Robust optimization in machine learning, 2011.

R. Carroll, L. A. Stefanski, D. Ruppert, and C. C. , Measurement error in nonlinear models, 2006.

A. Chambolle, R. A. De-vore, N. Lee, and B. J. Lucier, Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage, IEEE Transactions on Image Processing, vol.7, issue.3, pp.319-335, 1998.
DOI : 10.1109/83.661182

URL : http://www.math.purdue.edu/~lucier/692/finally.pdf

R. Chartrand and W. Yin, Iteratively reweighted algorithms for compressive sensing, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3869-3872, 2008.
DOI : 10.1109/ICASSP.2008.4518498

M. Chau, R. Cheng, B. Kao, and J. Ng, Uncertain Data Mining: An Example in Clustering Location Data, PAKDD 2006, LNAI 3918, pp.199-204, 2006.
DOI : 10.1007/11731139_24

B. B. Chaudhuria and P. R. Bhowmik, An approach of clustering data with noisy or imprecise feature measurement, Pattern Recognition Letters, vol.19, issue.14, pp.1307-1317, 1998.
DOI : 10.1016/S0167-8655(98)00112-3

Z. Chen and A. Cichocki, Nonnegative matrix factorization with temporal smoothness and/or spatial decorrelation constraints, pp.24-31, 2005.

S. Chen, D. Donoho, and M. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010

URL : http://www-stat.stanford.edu/~donoho/Reports/1995/30401.pdf

X. Chen, F. M. Xu, Y. , and Y. , Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization, SIAM Journal on Scientific Computing, vol.32, issue.5, pp.2832-2852, 2010.
DOI : 10.1137/090761471

M. Chu, F. Diele, R. Plemmons, and S. Ragni, Optimality, computation and interpretation of nonnegative matrix factorizations, SIAM Journal on Matrix Analysis, pp.4-8030, 2004.

A. Cichocki and R. Zdunek, Multilayer nonnegative matrix factorisation, Electronics Letters, vol.42, issue.16, pp.947-948, 2006.
DOI : 10.1049/el:20060983

A. Cichocki, R. Zdunek, and A. , Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization, Lecture Notes in Computer Science, vol.4666, pp.169-176, 2007.
DOI : 10.1007/978-3-540-74494-8_22

URL : http://www.bsp.brain.riken.jp/publications/2007/ICA-2007-org.pdf

A. Cichocki and A. Phan, Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.92, issue.3, pp.92-708, 2009.
DOI : 10.1587/transfun.E92.A.708

A. Cichocki, R. Zdunek, A. Phan, and A. , Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation, 2009.
DOI : 10.1002/9780470747278

J. E. Cohen and U. G. Rothblum, Nonnegative ranks, decompositions, and factorizations of nonnegative matrices, Linear Algebra and its Applications, vol.190, pp.149-168, 1993.
DOI : 10.1016/0024-3795(93)90224-C

R. Collobert, F. Sinz, J. Weston, and L. Bottou, Trading convexity for scalability, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143870

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society: Series B, vol.39, pp.1-38, 1977.

I. Dhillon and S. Sra, Generalized nonnegative matrix approximations with bregman divergences, Advances in Neural Information Processing Systems, pp.283-290, 2006.

C. Ding, T. Li, and J. M. , Convex and Semi-Nonnegative Matrix Factorizations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.1, pp.45-55, 2010.
DOI : 10.1109/TPAMI.2008.277

URL : http://ranger.uta.edu/~chqding/papers/Ding-Li-Jordan.pdf

M. Elad and M. Aharon, Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3745, 2006.
DOI : 10.1109/TIP.2006.881969

URL : http://www.cs.technion.ac.il/~elad/publications/journals/2005/KSVD_Denoising_IEEE_TIP.pdf

K. Engan, S. O. Aase, and J. H. Husoy, Method of optimal directions for frame design, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
DOI : 10.1109/ICASSP.1999.760624

J. Fan and R. Li, Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001.
DOI : 10.1198/016214501753382273

URL : http://www.stat.psu.edu/~rli/research/penlike.pdf

A. Fawzi, M. Davies, and P. Frossard, Dictionary learning for fast classification based on soft-thresholding. submitted to, International Journal of Computer Vision, p.1402, 1973.

V. Franc, V. Hlavac, and M. Navara, Sequential Coordinate-Wise Algorithm for the Non-negative Least Squares Problem, CAIP 2005: Computer Analysis of Images and Patterns, pp.407-414, 2005.
DOI : 10.1007/11556121_50

W. J. Fu, Penalized regression: the bridge versus the lasso, J. Comp. Graph. Stat, vol.7, pp.397-416, 1998.

G. Gasso, A. Rakotomamonjy, and S. Canu, Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming, IEEE Transactions on Signal Processing, vol.57, issue.12, pp.4686-4698, 2009.
DOI : 10.1109/TSP.2009.2026004

URL : https://hal.archives-ouvertes.fr/hal-00439453

N. Gillis and F. Glineur, Accelerated Multiplicative Updates and Hierarchical ALS Algorithms for Nonnegative Matrix Factorization, Neural Computation, vol.4029, issue.4, pp.1085-1105, 2012.
DOI : 10.1007/s10115-004-0194-1

N. Gillis and F. Glineur, Nonnegative Factorization and The Maximum Edge Biclique Problem, CORE Discussion, vol.64, 2008.

T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science, vol.286, issue.5439, pp.286-531, 1999.
DOI : 10.1126/science.286.5439.531

P. Gong and C. Zhang, Efficient Nonnegative Matrix Factorization via projected Newton method, Pattern Recognition, vol.45, issue.9, pp.3557-3565, 2012.
DOI : 10.1016/j.patcog.2012.02.037

E. F. Gonzalez and Y. Zhang, Accelerating the Lee-Seung algorithm for non-negative matrix factorization, 2005.

G. J. Gordon, R. V. Jensen, L. Hsiao, S. R. Gullans, J. E. Blumenstock et al., Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma, Cancer Res, issue.17, pp.62-4963, 2002.

I. F. Gorodnitsky and B. D. Rao, Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm, IEEE Transactions on Signal Processing, vol.45, issue.3, pp.600-616, 1997.
DOI : 10.1109/78.558475

R. Gribonval and M. Nielsen, Sparse representations in unions of bases, IEEE Transactions on Information Theory, vol.49, issue.12, pp.3320-3325, 2003.
DOI : 10.1109/TIT.2003.820031

URL : https://hal.archives-ouvertes.fr/inria-00071943

W. Guan and A. Gray, Sparse high-dimensional fractional-norm support vector machine via DC programming, Computational Statistics & Data Analysis, vol.67, pp.136-148, 2013.
DOI : 10.1016/j.csda.2013.01.020

N. Guan, D. Tao, Z. Lou, and B. Yuan, NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization, IEEE Transactions on Signal Processing, vol.60, issue.6, pp.2882-2898, 2012.
DOI : 10.1109/TSP.2012.2190406

F. Gullo and A. Tagarelli, Uncertain centroid based partitional clustering of uncertain data, Proceedings of the VLDB Endowment (ACM), pp.610-621, 2012.
DOI : 10.14778/2180912.2180914

I. Guyon, J. Weston, S. Barnhill, and V. N. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, pp.1-3, 2002.

I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature extraction, foundations and applications, Studies in Fuzziness and Soft Computing, 2006.

H. Hamdan and G. Govaert, Mixture Model Clustering of Uncertain Data, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., pp.879-884, 2005.
DOI : 10.1109/FUZZY.2005.1452510

URL : https://hal.archives-ouvertes.fr/hal-01377398

P. Hansen and B. Jaumard, Cluster analysis and mathematical programming, Mathematical Programming, pp.191-215, 1997.
DOI : 10.1007/978-3-642-80098-6_36

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2009.

M. Heiler, D. Cremers, and C. Schnörr, Efficient feature subset selection for support vector machines, 2001.

J. B. Hiriart-urruty and C. Lemarechal, Convex Analysis and Minimization Algorithms, 1993.
DOI : 10.1007/978-3-662-02796-7

N. Ho, Nonnegative Matrix Factorization: Algorithms and Applications, 2008.

W. E. Howard, Elementary matrix theory, 1966.

P. O. Hoyer, Nonnegative sparse coding, Proc. IEEE Workshop on Neural Netw. Signal Process, pp.557-565, 2002.

P. O. Hoyer, Non-negative Matrix Factorization with Sparseness Constraints, J. of Machine Learning Research, vol.5, pp.1457-1469, 2004.

J. Huang, J. Horowitz, M. , and J. , Asymptotic properties of bridge estimators in sparse high-dimensional regression models, The Annals of Statistics, vol.36, issue.2, pp.587-613, 2008.
DOI : 10.1214/009053607000000875

Y. Huang, H. Liu, and S. Zhou, Quadratic regularization projected Barzilai-Borwein method for nonnegative matrix factorization Data Min Knowl Disc, 2014.
DOI : 10.1007/s10618-014-0390-x

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/978-3-642-69024-2_27

A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

H. Kim and H. Park, Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis, Bioinformatics, vol.18, issue.6870, pp.1495-1502, 2007.
DOI : 10.1002/cem.889

H. Kim and H. Park, Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.2, pp.713-730, 2008.
DOI : 10.1137/07069239X

H. Kim and H. Park, Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons, SIAM Journal on Scientific Computing, vol.33, issue.6, pp.3261-3281, 2011.
DOI : 10.1137/110821172

URL : http://www.cc.gatech.edu/~hpark/papers/SISC_082117RR_Kim_Park.pdf

D. Kim, S. Sra, and I. S. Dhillon, Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem, Proceedings of the 2007 SIAM International Conference on Data Mining, 2007.
DOI : 10.1137/1.9781611972771.31

K. Knight and W. Fu, Asymptotics for lasso-type estimators, Ann. Stat, vol.28, pp.1356-1378, 2000.

N. Krause and Y. Singer, Leveraging the margin more carefully, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015344

K. Kreutz-delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee et al., Dictionary Learning Algorithms for Sparse Representation, Neural Computation, vol.15, issue.2, pp.349-396, 2003.
DOI : 10.1162/089976601300014385

H. W. Kuhn and R. E. Kuenne, An Efficient Algorithm for the Numerical Solution of the Generalized Weber Problem in Spatial Economics, Journal of Regional Science, vol.4, pp.21-34, 1962.
DOI : 10.1007/978-1-349-12752-8_10

M. Kumar and N. R. Patel, Clustering data with measurement errors, Computational Statistics & Data Analysis, vol.51, issue.12, pp.6084-6101, 2007.
DOI : 10.1016/j.csda.2006.12.012

H. M. Le, L. Thi, H. A. Nguyen, and M. C. , DCA based algorithms for feature selection in Semi-Supervised Support Vector Machines. Machine Learning and Data Mining in Pattern Recognition, LNAI 7988, pp.528-542, 2013.

H. M. Le, L. Thi, H. A. Pham-dinh, T. Huynh, and V. N. , Block Clustering Based on Difference of Convex Functions (DC) Programming and DC Algorithms, Neural Computation, vol.63, issue.7, pp.25-2776, 2013.
DOI : 10.1016/j.endm.2005.05.071

URL : https://hal.archives-ouvertes.fr/hal-01636679

H. M. Le, L. Thi, H. A. Nguyen, and M. C. , Sparse semi-supervised support vector machines by DC programming and DCA, Neurocomputing, vol.153, issue.4, pp.62-76, 2015.
DOI : 10.1016/j.neucom.2014.11.051

URL : https://hal.archives-ouvertes.fr/hal-01634225

M. , L. Hoai, and M. T. Ta, Dc programming and dca for solving minimum sum-of-squares clustering using weighted dissimilarity measures. Transaction Computational Collective Intelligence, pp.113-131, 2014.

H. A. Le-thi, Dc programming and dca, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01664024

H. A. Le-thi, Analyse num´erique des algorithmes de l'optimisation DC. Approches locale et globale. Codes et simulations numériques en grande dimension, 1994.

L. Thi and H. A. , ContributionàContributionà l'optimisation non convexe et l'optimisation globale: Théorie, Algorithmes et Applications, HabilitationàHabilitationà Diriger des Recherches, 1997.

H. A. Le-thi, T. Pham-dinh, and M. L. Dung, Exact penalty in d.c. programming, Vietnam Journal of Mathematics, vol.27, issue.2, pp.169-178, 1999.
URL : https://hal.archives-ouvertes.fr/hal-01664702

L. Thi and H. A. , An efficient algorithm for globally minimizing a quadratic function under convex quadratic constraints, Mathematical Programming, pp.401-426, 2000.

L. Thi and H. A. , A new approximation for the ? 0 -norm, 2012.

L. Thi, H. A. Belghiti, T. , P. Dinh, and T. , A new efficient algorithm based on DC programming and DCA for Clustering, Journal of Global Optimization, vol.37, pp.593-608, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01636755

L. Thi, H. A. Le, H. M. Nguyen, V. V. , P. Dinh et al., A DC programming approach for feature selection in support vector machines learning, Advances in Data Analysis and Classification, vol.3, issue.1???3, pp.259-278, 2008.
DOI : 10.1007/978-1-4757-2440-0

URL : https://hal.archives-ouvertes.fr/hal-01636751

L. Thi, H. A. Le, H. M. , P. Dinh, and T. , Optimization based DC programming and DCA for Hierarchical Clustering, European Journal of Operational Research, vol.183, issue.3, pp.1067-1085, 2007.

L. Thi, H. A. Nguyen, T. P. , P. Dinh, and T. , A continuous approach for solving the concave cost supply problem by combining DCA and B&B techniques, European Journal of Operational Research, vol.183, pp.1001-1012, 2007.

L. Thi, H. A. , L. Hoai, M. , P. Dinh et al., Fuzzy clustering based on nonconvex optimisation approaches using difference of convex (DC) functions algorithms, Advances in Data Analysis and Classification, vol.15, issue.3, pp.1-20, 2007.
DOI : 10.1515/9781400873173

URL : https://hal.archives-ouvertes.fr/hal-01636753

L. Thi, H. A. Huynh, V. N. , P. Dinh, and T. , Convergence Analysis of DC Algorithms for DC programming with subanalytic data, Research Report, National Institute for Applied Sciences, 2009.

L. Thi, H. A. Nguyen, V. V. Ouchani, and S. , Gene Selection for Cancer Classification Using DCA, Journal of Fonctiers of Computer Science and Technology, vol.3, issue.6, pp.62-72, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01664633

L. Thi, H. A. , P. Dinh, and T. , Solving a class of linearly constrained indefinite quadratic problems by DC algorithms, Journal of Global Optimization, vol.11, issue.3, pp.253-285, 1997.
DOI : 10.1023/A:1008288411710

URL : https://hal.archives-ouvertes.fr/hal-01636781

L. Thi, H. A. , P. Dinh, and T. , A branch-and-bound method via D.C. optimization algorithm and ellipsoidal technique for box constrained nonconvex quadratic programming problems, Journal of Global Optimization, vol.13, pp.171-206, 1998.
URL : https://hal.archives-ouvertes.fr/hal-01636780

H. A. Le-thi and T. Pham-dinh, Dc programming approach for solving the multidimensional scaling problem. Nonconvex Optimizations and Its Applications: Special Issue From Local to Global Optimization, pp.231-276, 2001.

L. Thi, H. A. , P. Dinh, and T. , DC Programming: Theory, Algorithms and Applications . The State of the Art (28 pages) Proceedings of The First International Workshop on Global Constrained Optimization and Constraint Satisfaction (Cocos' 02), 2002.

L. Thi, H. A. , P. Dinh, and T. , Large Scale Molecular Optimization From Distance Matrices by a D, C. Optimization Approach. SIAM Journal on Optimization, vol.4, issue.1, pp.77-116, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01636768

L. Thi, H. A. , P. Dinh, and T. , The DC (difference of convex functions) Programming and DCA revisited with DC models of real world nonconvex optimization problems, Annals of Operations Research, vol.133, pp.23-46, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01636759

L. Thi, H. A. , P. Dinh, and T. , A continuous approach for the concave cost supply problem via DC programming and DCA, Discrete Applied Mathematics, vol.156, issue.3, pp.325-338, 2008.
DOI : 10.1016/j.dam.2007.03.024

URL : https://hal.archives-ouvertes.fr/hal-01636752

L. Thi, H. A. , P. Dinh, and T. , Minimum Sum-of-Squares Clustering by DC Programming and DCA In, pp.327-340, 2009.

H. A. Le-thi, T. Pham-dinh, and V. N. Huynh, Exact penalty techniques in dc programming, Journal of Global Optimization, pp.1-27, 2011.

L. Thi, H. A. Pham-dinh, and T. , DC programming approaches for Distance Geometry problems. in " Distance Geometry: Theory, Methods and Applications, pp.225-290, 2013.
DOI : 10.1007/978-1-4614-5128-0_13

L. Thi, H. A. Pham-dinh, T. , N. Van, and T. , Combination between global and local methods for solving an optimization problem over the efficient set, European Journal of Operational Research, vol.142, issue.2, pp.258-270, 2002.
DOI : 10.1016/S0377-2217(01)00301-0

URL : https://hal.archives-ouvertes.fr/hal-01636770

L. Thi, H. A. Moeini, and M. , Long-Short Portfolio Optimization Under Cardinality Constraints by Difference of Convex Functions Algorithm, Journal of Optimization Theory & Applications, vol.27, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01636672

L. Thi, H. A. Nguyen, and M. C. , Efficient algorithms for Feature Selection in Multi-class Support Vector Machine. Advanced Computational Methods for Knowledge Engineering, Studies in Computational Intelligence 479, 2013.

L. Thi, H. A. Huynh, V. N. , P. Dinh, and T. , Exact penalty and error bounds in DC programming, Journal of Global Optimization, vol.92, issue.2, pp.509-535, 2012.
DOI : 10.1007/s101070100278

URL : https://hal.archives-ouvertes.fr/hal-01636730

L. Thi, H. A. Moeini, M. Pham-dinh, T. Joaquim, and J. , A DC programming approach for solving the??symmetric Eigenvalue Complementarity Problem, Computational Optimization and Applications, vol.431, issue.5???7, pp.1097-1117, 2012.
DOI : 10.1016/j.laa.2009.03.031

URL : https://hal.archives-ouvertes.fr/hal-01636682

L. Thi, H. A. Tran, D. Q. , P. Dinh, and T. , A DC programming approach for a class of bilevel programming problems and its application in Portfolio Selection, Numerical Algebra, Control and Optimization, vol.1, pp.167-185, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01636726

L. Thi, H. A. Le, H. M. Pham-dinh, T. Huynh, and V. N. , Binary classification via spherical separator by DC programming and DCA, Journal of Global Optimization, vol.15, issue.4, pp.1393-1407, 2013.
DOI : 10.1162/08997660360581958

URL : https://hal.archives-ouvertes.fr/hal-01636671

L. Thi, H. A. Nguyen, T. B. Le, and H. M. , Sparse signal recovery by Difference of Convex functions Algorithms, Lecture Notes in Computer Science, pp.387-397, 2013.

L. Thi, H. A. Vo, X. T. Pham-dinh, and T. , Robust Feature Selection for SVMs under Uncertain Data, Advances in Data Mining. Applications and Theoretical Aspects, pp.151-165, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01636653

L. Thi, H. A. Le, H. M. Pham-dinh, and T. , Feature Selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm, Machine Learning, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01636662

L. Thi, H. A. Pham-dinh, T. Vo, and X. T. , DC Programming and DCA for Nonnegative Matrix Factorization, Eds): ICCCI 2014, LNAI 8733, pp.573-582, 2014.
DOI : 10.1007/978-3-319-11289-3_58

L. Thi, H. A. Le, H. M. Pham-dinh, T. Lauer, and F. , A DC programming algorithm for switched linear regression, IEEE Transactions on Automatic Control, vol.99, 2014.

L. Thi, H. A. Huynh, V. N. Pham-dinh, and T. , DC Programming and DCA for solving general DC Programs, Proceedings of 2nd International Conference on Computer Science, Applied Mathematics and Applications, 2014.

L. Thi, H. A. Le, H. M. Pham-dinh, and T. , New and efficient DCA based algorithms for minimum sum-of-squares clustering, Pattern Recognition, vol.47, issue.1, pp.388-401, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01636670

L. Thi, H. A. Vo, X. T. Pham-dinh, and T. , Feature selection for linear SVMs under uncertain data: Robust optimization based on difference of convex functions algorithms, Neural Networks, vol.59, pp.36-50, 2014.
DOI : 10.1016/j.neunet.2014.06.011

URL : https://hal.archives-ouvertes.fr/hal-01636653

L. Thi, H. A. Nguyen, M. C. Pham-dinh, and T. , A DC Programming Approach for Finding Communities in Networks, Neural Computation, vol.86, issue.28004, pp.2827-2854, 2014.
DOI : 10.1140/epjb/e2007-00331-0

URL : https://hal.archives-ouvertes.fr/hal-01636651

L. Thi, H. A. Pham-dinh, T. Le, H. M. Vo, and X. T. , DC approximation approaches for sparse optimization, European Journal of Operational Research, vol.244, issue.1, pp.26-46, 2015.
DOI : 10.1016/j.ejor.2014.11.031

URL : https://hal.archives-ouvertes.fr/hal-01634220

H. Lee, A. Battle, R. Raina, and A. Y. Ng, Efficient sparse coding algorithms, Advances in Neural Information Processing Systems, vol.19, pp.801-808, 2007.

S. D. Lee, B. Kao, and R. Cheng, Reducing UK-Means to K-Means, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp.483-488, 2007.
DOI : 10.1109/ICDMW.2007.40

D. D. Lee and H. S. Seung, Unsupervised learning by convex and conic coding, Proceedings of the Conference on Neural Information Processing Systems, pp.515-521, 1997.

D. D. Lee and H. S. Seung, Learning the Parts of Objects by Nonnegative Matrix Factorization, Nature, vol.401, pp.788-791, 1999.

D. D. Lee and H. S. Seung, Algorithms for Non-negetive matrix factorization, Advances in Neural Information Processing Systems, pp.556-562, 2001.

S. Z. Li, X. Hou, H. Zhang, and Q. Cheng, Learning spatially localized, parts-based representation, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.207-212, 2001.
DOI : 10.1109/CVPR.2001.990477

URL : http://www.microsoft.com/china/research/group/mcomputing/CVPR_LNMF-4th.pdf

C. Lin, On the convergence of multiplicative update algorithm for non-negetive matrix factorization, IEEE Transactions on Neural Networks, 2007.

C. Lin, Projected Gradient Methods for Nonnegative Matrix Factorization, Neural Computation, vol.5, issue.10, pp.2756-2779, 2007.
DOI : 10.1007/BF01584660

Y. Liu and X. Shen, -Learning, Journal of the American Statistical Association, vol.101, issue.474, pp.500-509, 2006.
DOI : 10.1198/016214505000000781

Y. Liu and Y. F. Zheng, FS_SFS: A novel feature selection method for support vector machines, Pattern Recognition, vol.39, issue.7, pp.1333-1345, 2006.
DOI : 10.1016/j.patcog.2005.10.006

Y. Liu and Y. Wu, Penalties, Journal of Computational and Graphical Statistics, vol.16, issue.4, pp.782-798, 2007.
DOI : 10.1198/106186007X255676

W. Liu, N. Zheng, and X. Lu, Non-negative matrix factorization for visual coding, IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings . (ICASSP '03), pp.293-299, 2003.

J. B. Macqueen, Some methods for classification and analysis of multivariate observations, Proc. Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

J. Mairal, M. Elad, and G. Sapiro, Sparse Representation for Color Image Restoration, IEEE Transactions on Image Processing, vol.17, issue.1, pp.53-69, 2008.
DOI : 10.1109/TIP.2007.911828

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

J. Mairal, F. Bach, and J. Ponce, Sparse Modeling for Image and Vision Processing, Foundations and Trends?? in Computer Graphics and Vision, vol.8, issue.2-3, pp.85-283, 2014.
DOI : 10.1561/0600000058

URL : https://hal.archives-ouvertes.fr/hal-01081139

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.41-3397, 1993.
DOI : 10.1109/78.258082

URL : http://home.ustc.edu.cn/~zhanghan/cs/Mallat_Zhang93.pdf

S. Mallat, A wavelet tour of signal processing (second edition), 1999.

O. L. Mangasarian, Machine learning via polyhedral concave minimization Applied Mathematics and Parallel Computing ? Festschrift for Klaus Ritter, 1996.

C. Michelot, A finite algorithm for finding the projection of a point onto the canonical simplex of ? n, Journal of Optimization Theory and Applications, vol.11, issue.1, pp.195-200, 1986.
DOI : 10.1007/BF00938486

M. Mohri and A. M. Medina, Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve Sparse approximate solutions to linear systems, Proceeding ICML'14 Proceedings of the 31th international conference on Machine learning, pp.227-234, 1995.

J. Neumann, G. Schnörr, and G. Steidl, Combined SVM-Based Feature Selection and Classification, Machine Learning, pp.1-3, 2005.
DOI : 10.1515/9781400873173

URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-005-1505-9.pdf

W. Ngai, B. Kao, C. Chui, R. Cheng, M. Chau et al., Efficient Clustering of Uncertain Data, Sixth International Conference on Data Mining (ICDM'06), pp.436-445, 2006.
DOI : 10.1109/ICDM.2006.63

T. B. Nguyen, L. Thi, H. A. Le, H. M. Vo, and X. T. , DC approximation approach for L0?minimization in compressed sensing, ICCSAMA 2015: Proceedings of 3rd International Conference on Computer Science, Applied Mathematics and Applications, AISC 358, pp.37-48, 2015.

B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, pp.607-609, 1996.
DOI : 10.1038/381607a0

B. A. Olshausen and D. J. Field, Sparse coding with an overcomplete basis set: A strategy employed by V1?, Vision Research, vol.37, issue.23, pp.3311-3325, 1997.
DOI : 10.1016/S0042-6989(97)00169-7

C. Ong, L. Thi, and H. A. , Learning sparse classifiers with Difference of Convex functions Algorithms. Optimization Methods and Software, pp.830-854, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01636678

P. Paatero and U. Tapper, Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.18, issue.2, pp.111-126, 1994.
DOI : 10.1007/978-3-642-93295-3_112

R. Pant, T. B. Trafalis, and K. Barker, Support Vector Machine Classification of Uncertain and Imbalanced Data using Robust Optimization, Proceedings of the 15th WSEAS international conference on Computers, pp.369-374, 2011.

Y. C. Pati, R. Rezaifar, and P. S. Krishnaprasa, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, 1993.
DOI : 10.1109/ACSSC.1993.342465

V. P. Pauca, J. Piper, and P. R. , Nonnegative matrix factorization for spectral data analysis, Linear Algebra and its Applications, vol.416, issue.1, pp.29-47, 2006.
DOI : 10.1016/j.laa.2005.06.025

D. Peleg and R. Meir, A bilinear formulation for vector sparsity optimization, Signal Processing, vol.88, issue.2, pp.375-389, 2008.
DOI : 10.1016/j.sigpro.2007.08.015

J. Peng and Y. Xiay, A Cutting Algorithm for the Minimum Sum-of-Squared Error Clustering, Proceedings of the 2005 SIAM International Conference on Data Mining, 2005.
DOI : 10.1137/1.9781611972757.14

P. Dinh and T. , Algorithms for solving a class of non convex optimization problems. Methods of subgradients, 1986.

P. Dinh, T. , L. Thi, and H. A. , Convex analysis approach to DC programming: Theory, algorithms and applications, Acta Math. Vietnamica, vol.22, issue.1, pp.289-357, 1997.

P. Dinh, T. Le-thi, and H. A. , A DC optimization algorithms for solving the trustregion subproblem, SIAM J Optim, vol.8, issue.2, pp.476-505, 1998.

P. Dinh, T. Nguyen-canh, N. Le-thi, and H. A. , An efficient combined DCA and B&B using DC/SDP relaxation for globally solving binary quadratic programs, Journal of Global Optimization, vol.30, issue.1, pp.595-632, 2010.
DOI : 10.1007/978-1-4615-4381-7

URL : https://hal.archives-ouvertes.fr/hal-01636744

P. Dinh, T. Le-thi, and H. A. , Recent Advances in DC Programming and DCA, Transactions on Computational Collective Intelligence, vol.8342, pp.1-37, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01664024

D. N. Phan, M. C. Nguyen, and H. A. Le-thi, A dc programming approach for sparse linear discriminant analysis, Advanced Computational Methods for Knowledge Engineering of Advances in Intelligent Systems and Computing, pp.65-74

M. Protter and M. Elad, Image Sequence Denoising via Sparse and Redundant Representations, IEEE Transactions on Image Processing, vol.18, issue.1, pp.27-35, 2009.
DOI : 10.1109/TIP.2008.2008065

URL : http://www.cs.technion.ac.il/~elad/publications/journals/2007/VideoDenoising_IEEETIP.pdf

B. D. Rao and K. Kreutz-delgado, An affine scaling methodology for best basis selection, IEEE Transactions on Signal Processing, vol.47, issue.1, pp.87-200, 1999.
DOI : 10.1109/78.738251

URL : http://dsp.rice.edu/files/cs/Rao4.pdf

B. D. Rao, K. Engan, S. F. Cotter, . J. Palmer, and K. Kreutzdelgado, Subset selection in noise based on diversity measure minimization, IEEE Transactions on Signal Processing, vol.51, issue.3, pp.51-760, 2003.
DOI : 10.1109/TSP.2002.808076

F. Rinaldi, Mathematical Programming Methods for minimizing the zero norm over polyhedral sets, 2009.

F. Rinaldi, F. Schoen, and M. Sciandrone, Concave programming for minimizing the zero-norm over polyhedral sets, Computational Optimization and Applications, vol.3, issue.3, pp.467-486, 2010.
DOI : 10.1007/978-3-642-99789-1_13

R. Rockafellar, Convex Analysis, 1970.
DOI : 10.1515/9781400873173

R. Rubinstein, M. Zibulevsky, and M. Elad, Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation, IEEE Transactions on Signal Processing, vol.58, issue.3, pp.1553-1564, 2010.
DOI : 10.1109/TSP.2009.2036477

R. Sandler and M. Lindenbaum, Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.8, pp.1590-1602, 2011.
DOI : 10.1109/TPAMI.2011.18

M. Schmidt, G. Fung, and G. Rosales, Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches, Proceedings of Machine Learning: ECML 2007, pp.286-297, 2007.
DOI : 10.1007/978-3-540-74958-5_28

H. D. Sherali and J. Desai, A Global Optimization RLT-based Approach for Solving the Hard Clustering Problem, Journal of Global Optimization, vol.58, issue.1, pp.281-306, 2005.
DOI : 10.1080/01621459.1963.10500845

P. K. Shivaswamy, C. Bhattacharyya, and A. J. Smola, Second order cone programming approaches for handling missing and uncertain data, J Mach Learn Res, vol.7, pp.1238-1314, 2006.

K. Skretting and K. Engan, Recursive Least Squares Dictionary Learning Algorithm, IEEE Transactions on Signal Processing, vol.58, issue.4, pp.2121-2130, 2010.
DOI : 10.1109/TSP.2010.2040671

B. K. Sriperumbudur, D. A. Torres, and R. G. Lanckriet, Sparse eigen methods by D.C. programming, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.831-838, 2007.
DOI : 10.1145/1273496.1273601

URL : http://cosmal.ucsd.edu/~gert/papers/sparse_eigenmethods_icml07.pdf

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron et al., <title>A new compressive imaging camera architecture using optical-domain compression</title>, Computational Imaging IV, 2006.
DOI : 10.1117/12.659602

M. Tan, L. Wang, and I. W. Tsang, Learning sparse svm for feature selection on very high dimensional datasets, 2010.

M. Thiao, T. Pham-dinh, and H. A. Le-thi, DC Programming approach for solving a class of Nonconvex Programs dealing with zero-norm. Modelling, Computation and Optimization in Information Systems ans Management Science, pp.348-357, 2008.

M. Thiao, T. Pham-dinh, L. Thi, and H. A. , A DC programming approach for Sparse Eigenvalue Problem, Proceedings of the 27th International Conference on Machine Learning , ICML 2010, pp.1063-1070, 2010.

C. Thurau, K. Kersting, M. Wahabzada, and C. Bauckhage, Convex non-negative matrix factorization for massive datasets, Knowledge and Information Systems, vol.30, issue.11, pp.457-478, 2011.
DOI : 10.1007/978-1-4613-8431-1

R. Tibshirani, Regression shrinkage and selection via the lasso, J. Roy. Stat. Soc, vol.46, pp.431-439, 1996.

J. F. Toland, Duality in nonconvex optimization, Journal of Mathematical Analysis and Applications, vol.66, issue.2, pp.399-415, 1978.
DOI : 10.1016/0022-247X(78)90243-3

G. Trombettoni, I. Araya, B. Neveu, and G. Chabert, Inner regions and interval linearizations for global optimization, Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pp.99-104, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00648085

J. B. Urruty, Generalized differentiability duality and optimization for problem dealing with differences of convex functions, Lecture Notes in Economics and Mathematical Systems, vol.256, pp.260-277, 1986.

C. F. Van-loan, The ubiquitous Kronecker product, Journal of Computational and Applied Mathematics, vol.123, issue.1-2, pp.85-100, 2000.
DOI : 10.1016/S0377-0427(00)00393-9

S. A. Vavasis, On the Complexity of Nonnegative Matrix Factorization, SIAM Journal on Optimization, vol.20, issue.3, pp.1364-1377, 2009.
DOI : 10.1137/070709967

H. D. Vinod, Integer Programming and the Theory of Grouping, Journal of the American Statistical Association, vol.15, issue.5, pp.506-519, 1969.
DOI : 10.1080/01621459.1963.10500845

J. Weston, . Elisseeff, A. B. Scholkopf, and M. Tipping, Use of the Zero-Norm with Linear Models and Kernel Methods, Journal of Machine Learning Research, vol.3, pp.1439-1461, 2003.

J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio et al., Feature selection for SVMs, Neural Information Processing Systems, 2001.

Y. Wu and Y. Liu, Robust Truncated Hinge Loss Support Vector Machines, Journal of the American Statistical Association, vol.102, issue.479, pp.974-893, 2007.
DOI : 10.1198/016214507000000617

URL : http://compgen.unc.edu/ICASG/publications/RLeeSVM.pdf

K. Wu and Y. , Alternative c-means clustering algorithms, Pattern Recognition, vol.35, issue.10, pp.2267-2278, 2002.
DOI : 10.1016/S0031-3203(01)00197-2

A. E. Xavier and V. L. Xavier, Solving the minimum sum-of-squares clustering problem by hyperbolic smoothing and partition into boundary and gravitational regions, Pattern Recognition, vol.44, issue.1, pp.70-77, 2011.
DOI : 10.1016/j.patcog.2010.07.004

L. Yang and L. Wang, A class of semi-supervised support vector machines by DC programming, Advances in Data Analysis and Classification, vol.112, issue.15, pp.417-433, 2013.
DOI : 10.1016/j.talanta.2013.03.016

J. Yang, J. Wright, T. S. Huang, and Y. Ma, Image Super-Resolution Via Sparse Representation, IEEE Transactions on Image Processing, vol.19, issue.11, pp.2861-2873, 2010.
DOI : 10.1109/TIP.2010.2050625

A. L. Yuille and A. Rangarajan, The Concave-Convex Procedure, Neural Computation, vol.39, issue.4, pp.915-936, 2003.
DOI : 10.1162/08997660260028674

C. H. Zhang, Nearly unbiased variable selection under minimax concave penalty, The Annals of Statistics, vol.38, issue.2, pp.894-942, 2010.
DOI : 10.1214/09-AOS729

URL : http://doi.org/10.1214/09-aos729

T. Zhang, Some sharp performance bounds for least squares regression with L 1 regularization, The Annals of Statistics, vol.37, issue.5A, pp.2109-2144, 2009.
DOI : 10.1214/08-AOS659

H. H. Zhang, J. Ahn, X. Lin, and C. Park, Gene selection using support vector machines with non-convex penalty, Bioinformatics, vol.16, issue.6871, pp.88-95, 2006.
DOI : 10.1038/415530a

J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, 1-norm support vector machines, 2004.

H. Zou, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006.
DOI : 10.1198/016214506000000735

H. Zou and R. Li, One-step sparse estimates in nonconcave penalized likelihood models, The Annals of Statistics, vol.36, issue.4, pp.1509-1533, 2008.
DOI : 10.1214/009053607000000802