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Bioinformatics Advance Access published online on May 5, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm134
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares for Microarray Data Analysis

Hyunsoo Kim and Haesun Park

College of Computing, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332, USA

To whom correspondence should be addressed. Dr. Hyunsoo Kim, E-mail: hskim{at}cc.gatech.edu


  Abstract

Motivation: Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high dimensional data in a lower dimensional space.

Results: In this paper, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithmvia alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms.

Availability: The software is available as supplementary material.

Associate Editor: Prof. David Rocke

Received on November 2, 2006; revised on February 19, 2007; accepted on April 1, 2007

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[Abstract] [Full Text] [PDF]


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