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bioRxiv · 10.1101/321802

Fast Nonnegative Matrix Factorization andApplications to Pattern Extraction, Deconvolutionand Imputation

Lin, X.·Boutros, P. C.
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Abstract

Nonnegative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence, and cannot handle missing values. In addition, most NMF research focuses only on blind decompositions: decomposition without utilizing prior knowledge. We adapt the idea of sequential coordinate-wise descent to NMF to increase the convergence rate. Our NMF algorithm thus handles missing values naturally and integrates prior knowledge to guide NMF towards a more meaningful decomposition. To support its use, we describe a novel imputation-based method to determine the rank of decomposition. All our algorithms are implemented in the R package NNLM, which is freely available on CRAN.

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Lin, X., Boutros, P. C.. 2018-05-15. Fast Nonnegative Matrix Factorization andApplications to Pattern Extraction, Deconvolutionand Imputation. https://doi.org/10.1101/321802

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