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 article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1495-1502 |
| Number of pages | 8 |
| Journal | Bioinformatics |
| Volume | 23 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jun 15 2007 |
| Externally published | Yes |
Bibliographical note
Funding Information:We would like to thank Dr Chris Ding, Dr Jean-Philippe Brunet, Dr Yuan Gao, Prof. Lars Eldén and Prof. Robert J. Plemmons for their valuable comments. In particular, we would also like to thank Prof. Chih-Jen Lin, Prof. Paul Tseng and Prof. Luigi Grippo for discussions on the convergence property. This material is based upon work supported in part by the National Science Foundation Grants ACI-0305543 and CCF-0621889. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
