Cohen, Jeremy ; Université de Mons > Faculté Polytechnique > Mathématique et Recherche opérationnelle
Gillis, Nicolas ; Université de Mons > Faculté Polytechnique > Service de Mathématique et Recherche opérationnelle
Language :
English
Title :
A New Approach to Dictionary-Based Nonnegative Matrix Factorization
Publication date :
28 August 2017
Event name :
European Signal Processing Conference
Event place :
Kos, Greece
Event date :
2017
Journal title :
European Signal Processing Conference
Peer reviewed :
Peer reviewed
Research unit :
F151 - Mathématique et Recherche opérationnelle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
M. Udell, C. Horn, R. Zadeh, and S. Boyd, "Generalized low rank models", Foundations and Trends in Machine Learning, vol. 9, no. 1, pp. 1-118, 2016.
D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization", Nature, vol. 401, no. 6755, pp. 788-791, 1999.
J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, pp. 354-379, 2012.
W. K. Ma, J. M. Bioucas-Dias, T. H. Chan, N. Gillis, P. Gader, A. J. Plaza, A. Ambikapathi, and C. Y. Chi, "A signal processing perspective on hyperspectral unmixing: Insights from remote sensing", IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 67-81, 2014.
J. Nascimento and J. Dias, "Vertex component analysis: a fast algorithm to unmix hyperspectral data", IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, 2005.
M. C. U. Araújo, T. C. B. Saldanha, R. K. H. Galvao, T. Yoneyama, H. C. Chame, and V. Visani, "The successive projections algorithm for variable selection in spectroscopic multicomponent analysis", Chemometrics and Intelligent Laboratory Systems, vol. 57, no. 2, pp. 65-73, 2001.
H. Ren and C.-I. Chang, "Automatic spectral target recognition in hyperspectral imagery", IEEE Trans. on Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1232-1249, 2003.
T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, "A simplex volume maximization framework for hyperspectral endmember extraction", IEEE Trans. on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4177-4193, 2011.
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, no. 4, pp. 698-714, 2014.
E. Elhamifar, G. Sapiro, and R. Vidal, "See all by looking at a few: Sparse modeling for finding representative objects", in 2012 IEEE Conference on Computer Vision and Pattern Recognition. Institute of Electrical & Electronics Engineers (IEEE), 2012. [Online]. Available: http://dx.doi.org/10.1109/cvpr.2012.6247852
E. Esser, M. Moller, S. Osher, G. Sapiro, and J. Xin, "A convex model for nonnegative matrix factorization and dimensionality reduction on physical space", IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3239-3252, 2012.
V. Bittorf, B. Recht, E. Re, and J. Tropp, "Factoring nonnegative matrices with linear programs", in Advances in Neural Information Processing Systems (NIPS'12), 2012, pp. 1223-1231.
N. Gillis and R. Luce, "Robust near-separable nonnegative matrix factorization using linear optimization", J. Mach. Learn. Res., vol. 15, pp. 1249-1280, 2014.
-, "A fast gradient method for nonnegative sparse regression with self dictionary", arXiv:1610.01349, 2016.
A. Zare and K. Ho, "Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing", IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 95-104, 2014.
N. Gillis and F. Glineur, "Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization", Neural Comput., vol. 24, no. 4, pp. 1085-1105, 2012.
N. Gillis, "Successive nonnegative projection algorithm for robust nonnegative blind source separation", SIAM J. Imaging Sci., vol. 7, no. 2, pp. 1420-1450, 2014.
N. Gillis, D. Kuang, and H. Park, "Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization", IEEE Trans. Geosci. Remote Sensing, vol. 53, no. 4, pp. 2066-2078, 2015.
