On a general method for matrix factorisation applied to supervised classification

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.