Projection pursuit for exploratory supervised classification

Eun Kyung Lee, Dianne Cook, Sigbert Klinke, Thomas Lumley

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data. This article introduces new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.

Original languageEnglish
Pages (from-to)831-846
Number of pages16
JournalJournal of Computational and Graphical Statistics
Issue number4
StatePublished - Dec 2005

Bibliographical note

Funding Information:
This work was partially supported by the National Research Laboratory Program of Korea Science and Engineering Foundation.


  • Data mining
  • Discriminant analysis
  • Exploratory multivariate data analysis
  • Gene expression data


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