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

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dc.contributor.authorLong, Tran Vanen_US
dc.contributor.authorLinsen, Larsen_US
dc.contributor.editorL. Linsen and H. -C. Hege and B. Hamannen_US
dc.date.accessioned2014-02-01T16:09:59Z
dc.date.available2014-02-01T16:09:59Z
dc.date.issued2013en_US
dc.description.abstractDNA 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.en_US
dc.description.seriesinformationVisualization in Medicine and Life Sciencesen_US
dc.identifier.isbn978-3-905674-52-1en_US
dc.identifier.urihttps://doi.org/10.2312/PE.VMLS.VMLS2013.055-059en_US
dc.publisherThe Eurographics Associationen_US
dc.titleSupervised Kernel Principal Component Analysis for Visual Sample-based Analysis of Gene Expression Dataen_US
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