期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 14, 期 4, 页码 831-846出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/106186005X77702
关键词
data mining; discriminant analysis; exploratory multivariate data analysis; gene expression data
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.
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