@inproceedings{7c03646c7f934eedb2ca6038871e8fb6,
title = "Covariance and PCA for categorical variables",
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 using the Newton method. 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.",
author = "Hirotaka Niitsuma and Takashi Okada",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 ; Conference date: 18-05-2005 Through 20-05-2005",
year = "2005",
doi = "10.1007/11430919_61",
language = "English",
isbn = "3540260765",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "523--528",
booktitle = "Advances in Knowledge Discovery and Data Mining - 9th Pacific-Asia Conference, PAKDD 2005, Proceedings",
}