Covariance and PCA for Categorical Variables
Machine Learning
2007-11-29 v1
Abstract
Covariances 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
Keywords
Cite
@article{arxiv.0711.4452,
title = {Covariance and PCA for Categorical Variables},
author = {Hirotaka Niitsuma and Takashi Okada},
journal= {arXiv preprint arXiv:0711.4452},
year = {2007}
}
Comments
12 pages, 5 figures