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Covariance and PCA for Categorical Variables

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Advances in Knowledge Discovery and Data Mining (PAKDD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3518))

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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.

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Authors and Affiliations

  1. Department of Informatics, Kwansei Gakuin University, 2-1 Gakuen-cho, Sanda, 669-1323, Japan

    Hirotaka Niitsuma & Takashi Okada

Authors
  1. Hirotaka Niitsuma
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  2. Takashi Okada
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Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Asahidai 1-1, 923-12292, Nomi, Japan

    Tu Bao Ho

  2. University of Hong Kong, Pokfulam Road, Hong Kong, China

    David Cheung

  3. Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona, USA

    Huan Liu

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© 2005 Springer-Verlag Berlin Heidelberg

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Niitsuma, H., Okada, T. (2005). Covariance and PCA for Categorical Variables. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_61

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  • DOI: https://doi.org/10.1007/11430919_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26076-9

  • Online ISBN: 978-3-540-31935-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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