, Volume 72, Issue 2, pp 123-153
Date: 07 Jun 2011

Projection pursuit via white noise matrices

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Abstract

Projection pursuit is a technique for locating projections from high- to low-dimensional space that reveal interesting non-linear 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.

Lindsay’s research is supported by the NSF-DMS.