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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Li, Y., Ngom, A. (2013). Versatile Sparse Matrix Factorization and Its Applications in High-Dimensional Biological Data Analysis. In: Ngom, A., Formenti, E., Hao, JK., Zhao, XM., van Laarhoven, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2013. Lecture Notes in Computer Science(), vol 7986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39159-0_9
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DOI: https://doi.org/10.1007/978-3-642-39159-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39158-3
Online ISBN: 978-3-642-39159-0
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