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Mixture of Probabilistic Factor Analysis Model and Its Applications

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Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

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Abstract

In this paper, the regression analysis is treated when the output estimate may take more than one value. This is an extension of the usual regression analysis and such cases may happen when the output is affected by some unknown input. The stochastic model used in this paper is the mixture of probabilistic factor analysis model whose identification scheme has been already developed by Tipping and Bishop. We will show the usefulness of our method by a numerical example.

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References

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Author information

Authors and Affiliations

  1. Department of Information Science and Systems Engineering Faculty of Science and Engineering, Konan University, 8-9-1 Okamoto, Higashinada, 658-8501, Kobe, Japan

    Masahiro Tanaka

Authors
  1. Masahiro Tanaka
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Tanaka, M. (2001). Mixture of Probabilistic Factor Analysis Model and Its Applications. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_26

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  • DOI: https://doi.org/10.1007/3-540-44668-0_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

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