On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data

Nonnegative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H, where V ~ WH. It has been successfully applied in the analysis and interpretation of la...

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Vydáno v:Neural computation Ročník 26; číslo 6; s. 1128
Hlavní autoři: Devarajan, Karthik, Cheung, Vincent C K
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.06.2014
Témata:
Algorithms
Animals
Artificial Intelligence
Computational Biology
Electromyography
Humans
Muscle, Skeletal - physiology
Normal Distribution
Pattern Recognition, Automated
ISSN:1530-888X, 1530-888X
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Shrnutí:Nonnegative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H, where V ~ WH. It has been successfully applied in the analysis and interpretation of large-scale data arising in neuroscience, computational biology, and natural language processing, among other areas. A distinctive feature of NMF is its nonnegativity constraints that allow only additive linear combinations of the data, thus enabling it to learn parts that have distinct physical representations in reality. In this letter, we describe an information-theoretic approach to NMF for signal-dependent noise based on the generalized inverse gaussian model. Specifically, we propose three novel algorithms in this setting, each based on multiplicative updates, and prove monotonicity of updates using the EM algorithm. In addition, we develop algorithm-specific measures to evaluate their goodness of fit on data. Our methods are demonstrated using experimental data from electromyography studies, as well as simulated data in the extraction of muscle synergies, and compared with existing algorithms for signal-dependent noise.
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ISSN:1530-888X
1530-888X
DOI:10.1162/NECO_a_00576