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VOL. 2 | 2008 Projection pursuit for discrete data

Abstract

This paper develops projection pursuit for discrete data using the discrete Radon transform. Discrete projection pursuit is presented as an exploratory method for finding informative low dimensional views of data such as binary vectors, rankings, phylogenetic trees or graphs. We show that for most data sets, most projections are close to uniform. Thus, informative summaries are ones deviating from uniformity. Syllabic data from several of Plato’s great works is used to illustrate the methods. Along with some basic distribution theory, an automated procedure for computing informative projections is introduced.

Information

Published: 1 January 2008
First available in Project Euclid: 7 April 2008

zbMATH: 1166.62048
MathSciNet: MR2459955

Digital Object Identifier: 10.1214/193940307000000482

Subjects:
Primary: 44A12 , 62K10 , 90C08

Keywords: binary vector , discrete data , discrete Radon transform , least uniform partition , phylogenetic tree , Plato , Projection pursuit , ranking , syllable patterns

Rights: Copyright © 2008, Institute of Mathematical Statistics

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