Versatile sparse matrix factorization and its applications in high-dimensional biological data analysis

Authors:
Yifeng Li;Alioune Ngom
Affiliations:
School of Computer Science, University of Windsor, Windsor, Ontario, Canada;School of Computer Science, University of Windsor, Windsor, Ontario, Canada
Venue:
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Year:
2013

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Abstract

Non-negative matrix factorization and sparse representation models have been successfully applied in high-throughput biological data analysis. In this paper, we propose our versatile sparse matrix factorization (VSMF) model for biological data mining. We show that many well-known sparse models are specific cases of VSMF. Through tuning parameters, sparsity, smoothness, and non-negativity can be easily controlled in VSMF. Our computational experiments corroborate the advantages of VSMF.