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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nikulin, V. Tian-Hsiang Huang McLachlan, G.J. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Mathematics, and Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia (McLachlan, G.J.) || Department of Mathematics, University of Queensland, Brisbane, Australia (Nikulin, V.) || Institute of Information Management, National Cheng Kung University, Tainan, Taiwan (Tian-Hsiang Huang) |
| Abstract | In microarray data analysis, dimension reduction is an important consideration in the construction of a successful classification algorithm. As an alternative to feature selection, we use a well-known matrix factorisation method. For example, we can employ the popular singular-value decomposition (SVD) or nonnegative matrix factorization. In this paper, we consider a novel algorithm for gradient-based matrix factorisation (GMF). We compare GMF and SVD in their application to five gene expression datasets. The experimental results show that our method is faster, more stable, and sensitive. |
| Starting Page | 618 |
| Ending Page | 621 |
| File Size | 357123 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424483068 |
| e-ISBN | 9781424483075 |
| DOI | 10.1109/BIBM.2010.5706640 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Accuracy Symmetric matrices Conferences Colon Matrix decomposition Gene expression Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
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