Covariance and PCA for Categorical Variables

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dc.creatorNiitsuma, Hirotaka
dc.creatorOkada, Takashi
dc.date2007-11-28
dc.date.accessioned2026-07-07T08:45:48Z
dc.date.available2026-07-07T08:45:48Z
dc.descriptionCovariances from categorical variables are defined using a regular simplex expression for categories. The method follows the variance definition by Gini, and it gives the covariance as a solution of simultaneous equations. The calculated results give reasonable values for test data. A method of principal component analysis (RS-PCA) is also proposed using regular simplex expressions, which allows easy interpretation of the principal components. The proposed methods apply to variable selection problem of categorical data USCensus1990 data. The proposed methods give appropriate criterion for the variable selection problem of categorical
dc.description12 pages, 5 figures
dc.identifierhttps://arxiv.org/abs/0711.4452
dc.identifierhttp://arxiv.org/abs/0711.4452
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/143078
dc.subjectMachine Learning
dc.titleCovariance and PCA for Categorical Variables
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