On Nonnegative Matrix Factorization Algorithms for Signal-Dependent Noise with Application to Electromyography Data
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Devarajan-2014-On nonnegative matri.pdf
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Author(s)
Devarajan, Karthik
Cheung, Vincent Chi-Kwan
Date Issued
May 2014
Journal
Neural Computation
Publisher
MIT Press
Citation
Devarajan, Karthik, and Vincent C. K. Cheung. “On Nonnegative Matrix Factorization Algorithms for Signal-Dependent Noise with Application to Electromyography Data.” Neural Computation 26, no. 6 (June 2014): 1128–1168. © 2014 Massachusetts Institute of Technology
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1162/NECO_a_00576
