Supervised Kernel Principal Component Analysis for Visual Sample-based Analysis of Gene Expression Data

Abstract

DNA microarray technology has enabled researchers to simultaneously investigate thousands of genes over hundreds of samples. Automatic classification of such data faces the challenge of dealing with smaller number of samples compared to a larger dimensionality. Dimension reduction techniques are often applied to overcome this. Recently, a number of supervised dimension reduction techniques have been developed. We present a novel supervised dimension reduction technique called supervised kernel principal component analysis and demonstrate its effectiveness for visual representation and visual analysis of gene expression data.