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Versatile Sparse Matrix Factorization and Its Applications in High-Dimensional Biological Data Analysis

  • Yifeng Li
  • Alioune Ngom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)

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.

Keywords

versatile sparse matrix factorization non-negative matrix factorization sparse representation feature extraction feature selection biological processes identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yifeng Li
    • 1
  • Alioune Ngom
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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