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A projection pursuit index for large p small n data

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Abstract

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.

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Authors and Affiliations

  1. Department of Statistics, EWHA Womans University, Seoul, Korea

    Eun-Kyung Lee

  2. Department of Statistics, Iowa State University, Ames, IA, USA

    Dianne Cook

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  1. Eun-Kyung Lee
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  2. Dianne Cook
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Correspondence to Eun-Kyung Lee.

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Lee, EK., Cook, D. A projection pursuit index for large p small n data. Stat Comput 20, 381–392 (2010). https://doi.org/10.1007/s11222-009-9131-1

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  • DOI: https://doi.org/10.1007/s11222-009-9131-1

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