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

Long, Tran Van; Linsen, Lars

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

Files

055-059.pdf (131.83 KB)

Date

2013

Authors

Long, Tran Van
Linsen, Lars

Publisher

The Eurographics Association

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.

        @inproceedings{:10.2312/PE.VMLS.VMLS2013.055-059
,
booktitle = {Visualization in Medicine and Life Sciences
},
editor = {L. Linsen and H. -C. Hege and B. Hamann
},
title = {{Supervised Kernel Principal Component Analysis for Visual Sample-based Analysis of Gene Expression Data
}},
author = {Long, Tran Van and 
Linsen, Lars
},
year = {2013
},
publisher = {The Eurographics Association
},
ISBN = {978-3-905674-52-1
},
DOI = {/10.2312/PE.VMLS.VMLS2013.055-059
}
}