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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... (Update)
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BibTeX entry: (Update)
Daniel D. Lee and H. Sebastian Seung. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems 13, 2001. http://citeseer.ist.psu.edu/lee01algorithms.html More
@inproceedings{ lee00algorithms,
author = "Daniel D. Lee and H. Sebastian Seung",
title = "Algorithms for Non-negative Matrix Factorization",
booktitle = "{NIPS}",
pages = "556-562",
year = "2000",
url = "citeseer.ist.psu.edu/lee01algorithms.html" }
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