Projection pursuit via white noise matrices
 Guodong Hui,
 Bruce G. Lindsay
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Projection pursuit is a technique for locating projections from high to lowdimensional space that reveal interesting nonlinear features of a data set, such as clustering and outliers. The two key components of projection pursuit are the chosen measure of interesting features (the projection index) and its algorithm. In this paper, a white noise matrix based on the Fisher information matrix is proposed for use as the projection index. This matrix index is easily estimated by the kernel method. The eigenanalysis of the estimated matrix index provides a set of solution projections that are most similar to white noise. Application to simulated data and real data sets shows that our algorithm successfully reveals interesting features in fairly high dimensions with a practical sample size and low computational effort.
Inside
Within this Article
 Introduction
 Standardized Fisher information matrix
 White noise detection and testing
 Selection of H
 Examples
 Two final remarks
 Conclusion and future work
 References
 References
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 Title
 Projection pursuit via white noise matrices
 Journal

Sankhya B
Volume 72, Issue 2 , pp 123153
 Cover Date
 20101101
 DOI
 10.1007/s135710110008x
 Print ISSN
 09768386
 Online ISSN
 09768394
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Projection pursuit
 Fisher information matrix
 Eigenanalysis
 Authors

 Guodong Hui ^{(1)}
 Bruce G. Lindsay ^{(2)}
 Author Affiliations

 1. Genzyme Corporation, Framingham, MA, 01702, USA
 2. Department of Statistics, Pennsylvania State University, University Park, PA, 16802, USA