Title :
On a general method for matrix factorisation applied to supervised classification
Author :
Nikulin, Vladimir ; McLachlan, Geoffrey J.
Author_Institution :
Dept. of Math., Univ. of Queensland, Brisbane, QLD, Australia
Abstract :
We propose a general method for matrix factorization based on decomposition by parts. It can reduce the dimension of expression data from thousands of genes to several factors. Unlike classification and regression, matrix decomposition requires no response variable and thus falls into category of unsupervised learning methods. We demonstrate the effectiveness of this approach to the supervised classification of gene expression data.
Keywords :
matrix decomposition; pattern classification; unsupervised learning; gene expression data; matrix factorisation; supervised classification; unsupervised learning methods; Biological system modeling; Cancer; Gene expression; Linear regression; Mathematics; Matrix decomposition; Noise reduction; Support vector machine classification; Support vector machines; Unsupervised learning; cross-validation; gene expression data; gradient-based optimisation; matrix factorisation;
Conference_Titel :
Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009. IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-5121-0
DOI :
10.1109/BIBMW.2009.5332135
