{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/covariance-and-pca-for-categorical-variables","title":"Covariance and PCA for Categorical Variables","arxiv_id":"0711.4452","date":"2007-11-28","proceeding":null,"authors":["Hirotaka Niitsuma","Takashi Okada"],"abstract":"Covariances from categorical variables are defined using a regular simplex\nexpression for categories. The method follows the variance definition by Gini,\nand it gives the covariance as a solution of simultaneous equations. The\ncalculated results give reasonable values for test data. A method of principal\ncomponent analysis (RS-PCA) is also proposed using regular simplex expressions,\nwhich allows easy interpretation of the principal components. The proposed\nmethods apply to variable selection problem of categorical data USCensus1990\ndata. The proposed methods give appropriate criterion for the variable\nselection problem of categorical","url_abs":"http://arxiv.org/abs/0711.4452v1","url_pdf":"http://arxiv.org/pdf/0711.4452v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"covariance-and-pca-for-categorical-variables","repo_url":"https://github.com/Snowball119/Mixed_Data_PCA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"variable-selection","task_name":"Variable Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}