Name
Title | ||
|---|---|---|
A comparative study of two matrix factorization methods applied to the classification of gene expression data |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BIBM.2010.5706640 | Bioinformatics and Biomedicine |
Keywords | Field | DocType |
bioinformatics,biological techniques,data reduction,gradient methods,matrix algebra,molecular biophysics,singular value decomposition,SVD,classification algorithm,dimension reduction,gene expression data classification,gene expression datasets,gradient based matrix factorisation,matrix factorization methods,microarray data analysis,nonnegative matrix factorization,singular value decomposition | Dimensionality reduction,Matrix factorisation,Feature selection,Computer science,Theoretical computer science,Artificial intelligence,Singular value decomposition,Pattern recognition,Matrix decomposition,Symmetric matrix,Non-negative matrix factorization,Machine learning,Data reduction | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-4244-8307-5 | 1 |
PageRank | References | Authors |
0.37 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Nikulin | 1 | 99 | 17.28 |
| Tian-Hsiang Huang | 2 | 50 | 6.12 |
| McLachlan Geoffrey J. | 3 | 1787 | 126.70 |