A comparative study of two matrix factorization methods applied to the classification of gene expression rate

Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2010). A comparative study of two matrix factorization methods applied to the classification of gene expression rate. In: T. Park, L. Chen, L. Wong, S. Tsui, M. Ng and X. Hu, Proceedings of 2010 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, (618-621). 18-21 December 2010.


Author Nikulin, Vladimir
Huang, Tian-Hsiang
McLachlan, Geoffrey J.
Title of paper A comparative study of two matrix factorization methods applied to the classification of gene expression rate
Conference Paper Type Fully Published Paper
Conference name IEEE International Conference on Bioinformatics & Biomedicine    (ERA 2010 Rank C)
Conference location Hong Kong
Conference dates 18-21 December 2010
Proceedings title Proceedings of 2010 IEEE International Conference on Bioinformatics and Biomedicine
Editor T. Park
L. Chen
L. Wong
S. Tsui
M. Ng
X. Hu
Place published Los Alamitos, CA, U.S.A.
Publisher IEEE Computer Society
Publication date 2010
ISBN 9781424483068
Start page 618
End page 621
Total pages 4
Collection year 2011
Language eng
Abstract/Summary 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.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Session 4: Cross-Cutting Computational Methods and Bioinformatics Infrastructure

Document type: Conference Paper
Sub-type: Fully Published Paper
Collections: School of Mathematics and Physics
Official 2011 Collection
 
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Created: Tue, 22 Feb 2011, 16:18:16 EST by Kay Mackie on behalf of School of Mathematics & Physics