Statistics and Computing

, Volume 20, Issue 3, pp 381–392 | Cite as

A projection pursuit index for large p small n data



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.


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


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of StatisticsEWHA Womans UniversitySeoulKorea
  2. 2.Department of StatisticsIowa State UniversityAmesUSA

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