A projection pursuit index for large p small n data

Eun Kyung Lee, Dianne Cook

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


In high-dimensional data, one often seeks a few interesting low-dimensional projections which reveal important aspects of the data. Projection pursuit for classification finds projections that reveal differences between classes. Even though projection pursuit is used to bypass the curse of dimensionality, most indexes will not work well when there are a small number of observations relative to the number of variables, known as a large p (dimension) small n (sample size) problem. This paper discusses the relationship between the sample size and dimensionality on classification and proposes a new projection pursuit index that overcomes the problem of small sample size for exploratory classification.

Original languageEnglish
Pages (from-to)381-392
Number of pages12
JournalStatistics and Computing
Issue number3
StatePublished - 2010


  • Gene expression data analysis
  • Multivariate data
  • Penalized discriminant analysis
  • The curse of dimensionality


Dive into the research topics of 'A projection pursuit index for large p small n data'. Together they form a unique fingerprint.

Cite this