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A projection pursuit index for large p small n data

Title
A projection pursuit index for large p small n data
Authors
Lee E.-K.Cook D.
Ewha Authors
이은경
SCOPUS Author ID
이은경scopus
Issue Date
2010
Journal Title
Statistics and Computing
ISSN
0960-3174JCR Link
Citation
vol. 20, no. 3, pp. 381 - 392
Indexed
SCIE; SCOPUS WOS scopus
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. © 2009 Springer Science+Business Media, LLC.
DOI
10.1007/s11222-009-9131-1
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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