Projection pursuit for discrete data
Persi Diaconis, Julia Salzman
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.
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Primary Subjects: 44A12, 62K10, 90C08
Keywords: binary vector; discrete data; discrete Radon transform; least uniform partition; phylogenetic tree; Plato; projection pursuit; ranking; syllable patterns
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Permanent link to this document: http://projecteuclid.org/euclid.imsc/1207580088
Digital Object Identifier: doi:10.1214/193940307000000482
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