Multidimensional scaling with discrimination coefficients for supervised visualization of high-dimensional data

Daniel Berrar, Georg Ohmayer

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Visualization techniques for high-dimensional data sets play a pivotal role in exploratory analysis in a wide range of disciplines. A particularly challenging problem represents gene expression data based on microarray technology where the number of features (genes) typically exceeds 20,000, whereas the number of samples is frequently below 200. We investigated class-specific discrimination coefficients for each feature and each pair of classes for an effective nonlinear mapping to lower-dimensional space. We applied the technique to three microarray data sets and compared the projections to two-dimensional space with the results from a conventional multidimensional scaling method, a score plot resulting from principal component analysis, and projections from linear discriminant analysis. In the experiments, we observed that the discrimination coefficients allowed for an improved visualization of high-dimensional genomic data.

本文言語English
ページ(範囲)1211-1218
ページ数8
ジャーナルNeural Computing and Applications
20
8
DOI
出版ステータスPublished - 2011 11

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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