The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Generalized Divergence Measure for Nonnegative Matrix Factorization
Abstract
This letter presents a general parametric divergence measure. The metric includes as special cases quadratic error and Kullback-Leibler divergence. A parametric generalization of the two different multiplicative update rules for nonnegative matrix factorization by Lee and Seung (2001) is shown to lead to locally optimal solutions of the nonnegative matrix factorization problem with this new cost function. Numeric simulations demonstrate that the new update rule may improve the quadratic distance convergence speed. A proof of convergence is given that, as in Lee and Seung, uses an auxiliary function known from the expectation-maximization theoretical framework.
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Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Cited by
Csiszár’s Divergences for Non-negative Matrix Factorization: Family of New Algorithms
Cichocki, A., Zdunek, R., & Amari, S.-I. (2006) · Lecture Notes in Computer Science · doi:10.1007/11679363_5 · Cited by 271
Online Non-Negative Convolutive Pattern Learning for Speech Signals
Wang, D., Vipperla, R., Evans, N., & Zheng, T. F. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2222381 · Cited by 25
Metadata
- Authors
- Kompass, R.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2007-03
- Volume
- 19
- Issue
- 3
- Pages
- 780-791
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2055070149
- Cited by
- 191
- References
- 1
- FWCI
- 20.51
- Type
- journal article
- Language
- en
- Open access
- closed
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