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

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 n

SUBMITTER: Devarajan K 

PROVIDER: S-EPMC5548326 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature