E. Abbe, S. Afonso, G. Bandeira, and . Hall, Exact recovery in the stochastic block model, 2014.

H. Akaike, A new look at the statistical model identication, Automatic Control, IEEE Transactions on, vol.19, issue.6, p.716723, 1974.

S. Afonso and . Bandeira, Ten lectures and forty-two open problems in the mathematics of data science, 2015.

M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, NIPS, vol.14, p.585591, 2001.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.22, issue.7, p.719725, 2000.

C. Biernacki and S. Chrétien, Degeneracy in the maximum likelihood estimation of univariate gaussian mixtures with em, Statistics & probability letters, vol.61, issue.4, p.373382, 2003.

S. Boucheron, G. Lugosi, and P. Massart, A nonasymptotic theory of independence, Concentration inequalities, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00794821

J. Emmanuel, X. Candès, Y. Li, J. Ma, and . Wright, Robust principal component analysis?, Journal of the ACM (JACM), vol.58, issue.3, p.11, 2011.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, issue.1, p.18858, 1977.

O. Guédon and R. Vershynin, Community detection in sparse networks via grothendieck's inequality, Probability Theory and Related Fields, p.125, 2015.

S. Heimlicher, M. Lelarge, and L. Massoulié, Community detection in the labelled stochastic block model, 2012.

C. Helmberg and F. Rendl, A spectral bundle method for semidenite programming, SIAM Journal on Optimization, vol.10, issue.3, p.673696, 2000.
DOI : 10.1137/s1052623497328987

T. D. Hocking, A. Joulin, F. Bach, and J. Vert, Clusterpath an algorithm for clustering using convex fusion penalties, 28th international conference on machine learning, p.1, 2011.

. Anil-k-jain, Data clustering: 50 years beyond k-means, Pattern recognition letters, vol.31, issue.8, p.651666, 2010.

B. William and J. Lindenstrauss, Extensions of lipschitz mappings into a hilbert space, Contemporary mathematics, vol.26, pp.189-206, 1984.

I. Jollie, Principal component analysis, 2002.

N. Linial, E. London, and Y. Rabinovich, The geometry of graphs and some of its algorithmic applications, Combinatorica, vol.15, issue.2, p.215245, 1995.

G. Mclachlan and D. Peel, Finite mixture models, 2004.

E. Mossel, J. Neeman, and A. Sly, Stochastic block models and reconstruction, 2012.

M. Overton, Hanso: a hybrid algorithm for nonsmooth optimization, 2009.

P. Radchenko and G. Mukherjee, Consistent clustering using an _1 fusion penalty, 2014.

G. Schwarz, Estimating the dimension of a model, The annals of statistics, vol.6, issue.2, p.461464, 1978.

D. Kean-ming-tan and . Witten, Statistical properties of convex clustering, Electronic Journal of Statistics, vol.9, issue.2, p.23242347, 2015.

. Joel-a-tropp, Column subset selection, matrix factorization, and eigenvalue optimization, Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, p.978986, 2009.

U. V. Luxburg, A tutorial on spectral clustering, Statistics and computing, vol.17, issue.4, p.395416, 2007.

V. Vu, Singular vectors under random perturbation, Random Structures & Algorithms, vol.39, issue.4, p.526538, 2011.
DOI : 10.1002/rsa.20367
URL : http://arxiv.org/pdf/1004.2000

B. Wang, Y. Zhang, W. Sun, and Y. Fang, Sparse convex clustering, 2016.
DOI : 10.1080/10618600.2017.1377081
URL : http://arxiv.org/pdf/1601.04586

Q. Kilian, L. Weinberger, and . Saul, Unsupervised learning of image manifolds by semidenite programming, International Journal of Computer Vision, vol.70, issue.1, p.7790, 2006.

K. Hemant, A. Aggarwal, and . Majumdar, Hyperspectral image denoising using spatio-spectral total variation, IEEE Geoscience and Remote Sensing Letters, vol.13, issue.3, pp.442-446, 2016.

S. Arora, R. Ge, R. Kannan, and A. Moitra, Computing a nonnegative matrix factorization-provably, Proceedings of the forty-fourth annual ACM symposium on Theory of computing, pp.145-162, 2012.
DOI : 10.1137/130913869
URL : http://arxiv.org/pdf/1111.0952.pdf

C. Bauckhage, A purely geometric approach to non-negative matrix factorization, pp.125-136, 2014.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in, Machine Learning, vol.3, issue.1, pp.1-122, 2011.

N. Gillis, The why and how of nonnegative matrix factorization, Regularization, Optimization, Kernels, and Support Vector Machines, vol.12, p.257, 2014.

T. Goldstein, O. Brendan, S. Donoghue, R. Setzer, and . Baraniuk, Fast alternating direction optimization methods, SIAM Journal on Imaging Sciences, vol.7, issue.3, pp.1588-1623, 2014.
DOI : 10.1137/120896219

O. Patrik and . Hoyer, Non-negative matrix factorization with sparseness constraints, The Journal of Machine Learning Research, vol.5, pp.1457-1469, 2004.

M. Iordache, M. José, A. Bioucas-dias, and . Plaza, Total variation spatial regularization for sparse hyperspectral unmixing, Geoscience and Remote Sensing, IEEE Transactions on, vol.50, issue.11, pp.4484-4502, 2012.
DOI : 10.1109/tgrs.2012.2191590
URL : http://www.lx.it.pt/~bioucas/files/ieee_tgrs_12_sparse_unmix_tv.pdf

V. Krishnamurthy-and-alexandre-d'aspremont, Convex algorithms for nonnegative matrix factorization, 2012.

D. Daniel, H. Lee, and . Seung, Algorithms for non-negative matrix factorization, Advances in neural information processing systems, pp.556-562, 2001.

M. P. José, . Nascimento, . José, and . Dias, Vertex component analysis: A fast algorithm to unmix hyperspectral data, IEEE transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.898-910, 2005.

. Michael-k-ng, H. Raymond, W. Chan, and . Tang, A fast algorithm for deblurring models with neumann boundary conditions, SIAM Journal on Scientific Computing, vol.21, issue.3, pp.851-866, 1999.

S. Leonid-i-rudin, E. Osher, and . Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1, pp.259-268, 1992.

A. Stephen and . Vavasis, On the complexity of nonnegative matrix factorization, SIAM Journal on Optimization, vol.20, issue.3, pp.1364-1377, 2009.

R. Warren and . Osher, Hyperspectral unmixing by the alternating direction method of multipliers, Inverse Problems and Imaging, vol.14, issue.3, pp.0-00, 2015.
DOI : 10.3934/ipi.2015.9.917
URL : http://www.aimsciences.org/journals/doIpChk.jsp?paperID=11487&mode=full

J. Yang, W. Yin, Y. Zhang, and Y. Wang, A fast algorithm for edge-preserving variational multichannel image restoration, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.569-592, 2009.
DOI : 10.1137/080730421
URL : http://www.caam.rice.edu/%7Eyzhang/reports/tr0809.pdf

Y. Zhang, An alternating direction algorithm for nonnegative matrix factorization, 2010.

S. Aaronson, G. Kuperberg, and C. Et-granade, The complexity zoo, 2015.

A. , D. Deshpande, A. Hansen, P. Popat, and P. , Np-hardness of euclidean sum-of-squares clustering, Machine learning, vol.75, issue.2, pp.245-248, 2009.

A. Anandkumar, R. Ge, D. Hsu, and S. Et-kakade, A tensor spectral approach to learning mixed membership community models, Conference on Learning Theory, pp.867-881, 2013.

A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky, Tensor decompositions for learning latent variable models, Journal of Machine Learning Research, vol.15, pp.2773-2832, 2014.
DOI : 10.21236/ada604494
URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a604494.pdf

J. Anderson, N. Goyal, and L. Et-rademacher, Efficient learning of simplices, Conference on Learning Theory, pp.1020-1045, 2013.

A. M. Aragón, A c++ 11 implementation of arbitrary-rank tensors for high-performance computing, Computer Physics Communications, vol.185, issue.6, pp.1681-1696, 2014.

M. C. Araújo, T. C. Saldanha, R. K. Galvao, T. Yoneyama, H. C. Chame et al., The successive projections algorithm for variable selection in spectroscopic multicomponent analysis, vol.57, pp.65-73, 2001.

S. Arora and B. Barak, Computational complexity : a modern approach, 2009.

S. Arora, R. Ge, R. Kannan, and A. Moitra, Computing a nonnegative matrix factorization-provably, Proceedings of the forty-fourth annual ACM symposium on Theory of computing, pp.145-162, 2012.

D. Arthur and S. Vassilvitskii, k-means++ : The advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp.1027-1035, 2007.

B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S. Vassilvitskii, Scalable k-means++, Proceedings of the VLDB Endowment, vol.5, pp.622-633, 2012.

C. Bauckhage, A purely geometric approach to non-negative matrix factorization, LWA, pp.125-136, 2014.

J. M. Bioucas-dias, A variable splitting augmented lagrangian approach to linear spectral unmixing, Hyperspectral Image and Signal Processing : Evolution in Remote Sensing, 2009. WHISPERS'09. First Workshop on, pp.1-4, 2009.

J. M. Bioucas-dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du et al., Hyperspectral unmixing overview : Geometrical, statistical, and sparse regression-based approaches. IEEE journal of selected topics in applied earth observations and remote sensing, vol.5, pp.354-379, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00760787

S. Boucheron, G. Lugosi, and P. Massart, A nonasymptotic theory of independence, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00794821

N. Boumal, V. Voroninski, and A. Bandeira, The non-convex burermonteiro approach works on smooth semidefinite programs, Advances in Neural Information Processing Systems, pp.2757-2765, 2016.

C. Boutsidis and E. Gallopoulos, Svd based initialization : A head start for nonnegative matrix factorization, Pattern Recognition, vol.41, issue.4, pp.1350-1362, 2008.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Et-eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Machine Learning, vol.3, pp.1-122, 2011.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions, vol.23, issue.11, pp.1222-1239, 2001.

R. Bro, Parafac. tutorial and applications. Chemometrics and intelligent laboratory systems, vol.38, pp.149-171, 1997.
URL : https://hal.archives-ouvertes.fr/hal-02141162

S. Burer, On the copositive representation of binary and continuous nonconvex quadratic programs, Mathematical Programming, vol.120, issue.2, pp.479-495, 2009.

S. Burer and R. D. Monteiro, A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization, Mathematical Programming, vol.95, issue.2, pp.329-357, 2003.

E. J. Candès, X. Li, Y. Ma, and J. Wright, Robust principal component analysis, Journal of the ACM (JACM), vol.58, issue.3, p.11, 2011.

J. Cardoso and A. Souloumiac, Blind beamforming for non-gaussian signals, IEE proceedings F (radar and signal processing, vol.140, pp.362-370, 1993.

J. Cardoso and A. Souloumiac, Jacobi angles for simultaneous diagonalization, SIAM journal on matrix analysis and applications, vol.17, issue.1, pp.161-164, 1996.

S. Chrétien, C. Dombry, and A. Et-faivre, A semi-definite programming approach to low dimensional embedding for unsupervised clustering, 2016.

A. Çivril and M. Et-magdon-ismail, On selecting a maximum volume submatrix of a matrix and related problems, Theoretical Computer Science, pp.4801-4811, 2009.

P. L. Combettes and J. Pesquet, Proximal splitting methods in signal processing. In Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00643807

S. A. Cook, The complexity of theorem-proving procedures, Proceedings of the third annual ACM symposium on Theory of computing, pp.151-158, 1971.

M. D. Craig, Minimum-volume transforms for remotely sensed data, IEEE Transactions on Geoscience and Remote Sensing, vol.32, issue.3, pp.542-552, 1994.

L. De-lathauwer, B. De-moor, and J. Vandewalle, Independent component analysis based on higher-order statistics only, Proceedings., 8th IEEE Signal Processing Workshop on (Cat. No. 96TB10004, pp.356-359, 1996.

L. De-lathauwer, B. De-moor, and J. Vandewalle, Computation of the canonical decomposition by means of a simultaneous generalized schur decomposition, SIAM journal on Matrix Analysis and Applications, vol.26, issue.2, pp.295-327, 2004.

P. Diaconis, R. Graham, and W. M. Kantor, The mathematics of perfect shuffles, Advances in applied mathematics, vol.4, issue.2, pp.175-196, 1983.

J. A. Ellis, H. Fan, and J. Shallit, The cycles of the multiway perfect shuffle permutation, Discrete Mathematics & Theoretical Computer Science, vol.5, issue.1, pp.169-180, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00958980

A. Faivre and C. Dombry, Total variation regularized non-negative matrix factorization for smooth hyperspectral unmixing, 2017.

S. Fiorini, S. Massar, S. Pokutta, H. R. Tiwary, and R. D. Wolf, Exponential lower bounds for polytopes in combinatorial optimization, Journal of the ACM (JACM), vol.62, issue.2, p.17, 2015.

L. Frommhold, Collision-induced absorption in gases, vol.2, 2006.

E. Ghadimi, A. Teixeira, I. Shames, and M. Et-johansson, Optimal parameter selection for the alternating direction method of multipliers (admm) : quadratic problems, IEEE Transactions on Automatic Control, vol.60, issue.3, pp.644-658, 2015.

N. Gillis and S. A. Vavasis, Fast and robust recursive algorithmsfor separable nonnegative matrix factorization, IEEE transactions on pattern analysis and machine intelligence, vol.36, pp.698-714, 2014.
DOI : 10.1109/tpami.2013.226
URL : http://arxiv.org/pdf/1208.1237

M. X. Goemans and D. P. Et-williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, Journal of the ACM (JACM), vol.42, issue.6, pp.1115-1145, 1995.

G. H. Golub and C. F. Et-van-loan, Matrix computations, vol.3, 2012.

K. Goto and R. A. Et-geijn, Anatomy of high-performance matrix multiplication, ACM Transactions on Mathematical Software (TOMS), vol.34, issue.3, p.12, 2008.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on geoscience and remote sensing, vol.26, issue.1, pp.65-74, 1988.

O. Guédon and R. Et-vershynin, Community detection in sparse networks via grothendieck's inequality. Probability Theory and Related Fields, vol.165, pp.1025-1049, 2016.

G. Guennebaud and B. Jacob, Eigen v3, 2010.

N. Halko, P. Martinsson, Y. Shkolnisky, and M. Tygert, An algorithm for the principal component analysis of large data sets, SIAM Journal on Scientific computing, vol.33, issue.5, pp.2580-2594, 2011.

G. Hamerly, Making k-means even faster, SDM, pp.130-140, 2010.
DOI : 10.1137/1.9781611972801.12
URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972801.12

J. Hartmann, C. Boulet, and D. Robert, Collisional effects on molecular spectra : laboratory experiments and models, consequences for applications, 2008.

R. Heylen, D. Burazerovic, and P. Scheunders, Non-linear spectral unmixing by geodesic simplex volume maximization, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, pp.534-542, 2011.
DOI : 10.1109/jstsp.2010.2088377

P. O. Hoyer, Non-negative matrix factorization with sparseness constraints, Journal of machine learning research, vol.5, pp.1457-1469, 2004.

K. Huang, N. D. Sidiropoulos, and A. Swami, Non-negative matrix factorization revisited : Uniqueness and algorithm for symmetric decomposition, IEEE Transactions on Signal Processing, vol.62, issue.1, pp.211-224, 2014.
DOI : 10.1109/tsp.2013.2285514
URL : http://www.ece.umn.edu/~nikos/06630130.pdf

M. Iordache, J. M. Bioucas-dias, and A. Plaza, Total variation spatial regularization for sparse hyperspectral unmixing. Geoscience and Remote Sensing, IEEE Transactions on, vol.50, issue.11, pp.4484-4502, 2012.
DOI : 10.1109/tgrs.2012.2191590
URL : http://www.lx.it.pt/~bioucas/files/ieee_tgrs_12_sparse_unmix_tv.pdf

M. Iordache, A. Plaza, and J. Bioucas-dias, On the use of spectral libraries to perform sparse unmixing of hyperspectral data, Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), pp.1-4, 2010.

V. Kaibel, Extended formulations in combinatorial optimization, 2011.

N. Keshava and J. F. Et-mustard, Spectral unmixing. IEEE signal processing magazine, vol.19, pp.44-57, 2002.

D. Kim, S. Sra, and I. S. Et-dhillon, Fast newton-type methods for the least squares nonnegative matrix approximation problem, Proceedings of the 2007 SIAM international conference on data mining, pp.343-354, 2007.
DOI : 10.1137/1.9781611972771.31
URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972771.31

T. G. Kolda and B. W. Bader, Tensor decompositions and applications, SIAM review, vol.51, issue.3, pp.455-500, 2009.
DOI : 10.1137/07070111x
URL : http://csmr.ca.sandia.gov/~tgkolda/pubs/bibtgkfiles/SAND2007-6702.pdf

V. Krishnamurthy and A. Et-d'aspremont, Convex algorithms for nonnegative matrix factorization, 2012.

V. Kuleshov, A. Chaganty, and P. Liang, Tensor factorization via matrix factorization, Artificial Intelligence and Statistics, pp.507-516, 2015.

B. Kulis, A. C. Surendran, and J. C. Platt, Fast low-rank semidefinite programming for embedding and clustering, International Conference on Artificial Intelligence and Statistics, pp.235-242, 2007.

D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, Advances in neural information processing systems, pp.556-562, 2001.

J. Li, A. Agathos, D. Zaharie, J. M. Bioucas-dias, A. Plaza et al., Minimum volume simplex analysis : A fast algorithm for linear hyperspectral unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.9, pp.5067-5082, 2015.

L. Lovász, Semidefinite programs and combinatorial optimization, Recent advances in algorithms and combinatorics, pp.137-194, 2003.

T. Maehara and K. Murota, Algorithm for error-controlled simultaneous block-diagonalization of matrices, SIAM Journal on Matrix Analysis and Applications, vol.32, issue.2, pp.605-620, 2011.
DOI : 10.1137/090779966

J. Marot and S. Et-bourennane, Fast tensor signal filtering using fixed point algorithm, Acoustics, Speech and Signal Processing, pp.921-924, 2008.
DOI : 10.1109/icassp.2008.4517761
URL : https://hal.archives-ouvertes.fr/hal-00201974

A. Moitra, An almost optimal algorithm for computing nonnegative rank, SIAM Journal on Computing, vol.45, issue.1, pp.156-173, 2016.

M. D. Mura, A. Villa, J. A. Benediktsson, J. Chanussot, and L. Et-bruzzone, Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. Geoscience and Remote Sensing Letters, vol.8, pp.542-546, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00578886

J. M. Nascimento and J. M. Dias, Vertex component analysis : A fast algorithm to unmix hyperspectral data, IEEE transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.898-910, 2005.

J. M. Nascimento and J. M. Dias, Does independent component analysis play a role in unmixing hyperspectral data, 2005.

P. Netrapalli, U. Niranjan, S. Sanghavi, A. Anandkumar, and P. Jain, Non-convex robust pca, Advances in Neural Information Processing Systems, pp.1107-1115, 2014.

A. Packer, Np-hardness of largest contained and smallest containing simplices for v-and h-polytopes, Discrete and Computational Geometry, vol.28, issue.3, pp.349-377, 2002.

B. Park, W. Windham, K. Lawrence, and D. Smith, Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm, Biosystems Engineering, vol.96, issue.3, pp.323-333, 2007.

P. A. Parrilo, Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization, 2000.

G. Pataki, On the rank of extreme matrices in semidefinite programs and the multiplicity of optimal eigenvalues, Mathematics of operations research, vol.23, issue.2, pp.339-358, 1998.

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D : Nonlinear Phenomena, vol.60, issue.1, pp.259-268, 1992.

Y. Saad, Iterative methods for sparse linear systems, SIAM, 2003.

H. Sato, Riemannian newton's method for joint diagonalization on the stiefel manifold with application to ica, 2014.

Y. Tarabalka, J. Chanussot, J. A. Benediktsson, J. Angulo, and M. Fauvel, Segmentation and classification of hyperspectral data using watershed, Geoscience and Remote Sensing Symposium, vol.3, p.652, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00372311

Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Et-benediktsson, , 2010.

, Svm-and mrf-based method for accurate classification of hyperspectral images, IEEE Geoscience and Remote Sensing Letters, vol.7, issue.4, pp.736-740

F. J. Theis, T. P. Cason, and P. Et-absil, Soft dimension reduction for ica by joint diagonalization on the stiefel manifold, Independent Component Analysis and Signal Separation, pp.354-361, 2009.

C. Thurau, K. Kersting, M. Wahabzada, and C. Et-bauckhage, Descriptive matrix factorization for sustainability adopting the principle of opposites, Data Mining and Knowledge Discovery, vol.24, issue.2, pp.325-354, 2012.

F. G. Van-zee and R. A. Van-de-geijn, BLIS : A framework for rapidly instantiating BLAS functionality, ACM Transactions on Mathematical Software, vol.41, issue.3, p.33, 2015.

S. A. Vavasis, On the complexity of nonnegative matrix factorization, SIAM Journal on Optimization, vol.20, issue.3, pp.1364-1377, 2009.

S. Velasco-forero and J. Angulo, Classification of hyperspectral images by tensor modeling and additive morphological decomposition, Pattern Recognition, vol.46, issue.2, pp.566-577, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00751338

R. Warren and S. Osher, Hyperspectral unmixing by the alternating direction method of multipliers, Inverse Problems and Imaging, vol.14, issue.3, pp.0-00, 2015.

K. Q. Weinberger, B. Packer, and L. K. Saul, Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization, AISTATS, 2005.

M. E. Winter, N-findr : An algorithm for fast autonomous spectral endmember determination in hyperspectral data, SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, pp.266-275, 1999.
DOI : 10.1117/12.366289

M. Yannakakis, Expressing combinatorial optimization problems by linear programs, Proceedings of the twentieth annual ACM symposium on Theory of computing, pp.223-228, 1988.
DOI : 10.1016/0022-0000(91)90024-y
URL : https://doi.org/10.1016/0022-0000(91)90024-y

P. N. Yianilos, Data structures and algorithms for nearest neighbor search in general metric spaces, Proceedings of the Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1993.

Y. Zhang, An alternating direction algorithm for nonnegative matrix factorization, Rice Technical Report, 2010.

, Liste des publications et communications

S. Chrétien, C. Dombry, and A. Faivre, A Semi-Definite Programming approach to low dimensional embedding for unsupervised clustering, 2017.

A. Faivre and C. Dombry, Total variation regularized non-negative matrix factorization for smooth hyperspectral unmixing, 2017.

, A SemiDefinite Programming approach to Gaussian Mixture based clustering, 2016.