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Versatile sparse matrix factorization: Theory and applications.
- Source :
-
Neurocomputing . Dec2014, Vol. 145, p23-29. 7p. - Publication Year :
- 2014
-
Abstract
- In the recent years, non-negative matrix factorization and sparse representation models have been successfully applied in high-throughput biological data analysis due to its interpretability and robustness to noise. In this paper, we propose a unified matrix factorization model, coined versatile sparse matrix factorization (VSMF) model, for biological data analysis. We discuss the modelling, optimization, and applications of VSMF. We show that many well-known sparse matrix factorization models are specific cases of our VSMF. Through tuning parameters, sparsity, smoothness, and non-negativity can be easily controlled in VSMF. Our computational experiments for feature extraction, feature selection, and clustering corroborate the advantages of VSMF. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 145
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 97843157
- Full Text :
- https://doi.org/10.1016/j.neucom.2014.05.076