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

Daniel Berrar, Georg Ohmayer

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1211-1218
Number of pages8
JournalNeural Computing and Applications
Volume20
Issue number8
DOIs
Publication statusPublished - 2011 Nov 1

Keywords

  • Discrimination coefficients
  • Linear discriminant analysis
  • Microarrays
  • Multidimensional scaling
  • Principal component analysis
  • Visualization

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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