Algorithms for Non-negative Matrix Factorization

by: Daniel D Lee, Sebastian H Seung

(2000), pp. 556-562.

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摘要

Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary...

@inproceedings{citeulike:531251, abstract = {Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary...}, author = {Lee, Daniel D. and Seung, Sebastian H. }, booktitle = {NIPS}, citeulike-article-id = {531251}, keywords = {dimensionality, factorization, matrix, non-negative, reduction}, pages = {556--562}, posted-at = {2008-06-13 14:57:49}, priority = {2}, title = {Algorithms for Non-negative Matrix Factorization}, url = {http://citeseer.ist.psu.edu/lee01algorithms.html}, year = {2000} }

RIS record

TY - CONF ID - citeulike:531251 L3 - citeulike-article-id:531251 N2 - Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary... EP - 562 TI - Algorithms for Non-negative Matrix Factorization T2 - NIPS SP - 556 KW - dimensionality KW - factorization KW - matrix KW - non-negative KW - reduction AU - Lee, Daniel AU - Seung, Sebastian PY - 2000/// UR - http://citeseer.ist.psu.edu/lee01algorithms.html ER -

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