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A Comparative Study on Three MAP Factor Estimate Approaches for NFA

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Intelligent Data Engineering and Automated Learning — IDEAL 2002 (IDEAL 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2412))

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Abstract

In this paper we comparatively study three MAP factor estimate approaches, i.e., iterative fixed posteriori approximation, gradient descent approach, and conjugate gradient algorithm, for the non-Gaussian factor analysis (NFA). With the so-called Gaussian approximation as initialization, the iterative fixed posteriori approximation is empirically found to be the best one among them.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, P.R. China

    Zhiyong Liu & Lei Xu

Authors
  1. Zhiyong Liu
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  2. Lei Xu
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Electronics, UMIST, Manchester, M60 1QD, UK

    Hujun Yin , Nigel Allinson  & Richard Freeman ,  & 

  2. Department of Computation, UMIST, Manchester, M60 1QD, UK

    John Keane

  3. Department of Biomolecular Science, UMIST, Manchester, M60 1QD, UK

    Simon Hubbard

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© 2002 Springer-Verlag Berlin Heidelberg

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Liu, Z., Xu, L. (2002). A Comparative Study on Three MAP Factor Estimate Approaches for NFA. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_55

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  • DOI: https://doi.org/10.1007/3-540-45675-9_55

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44025-3

  • Online ISBN: 978-3-540-45675-9

  • eBook Packages: Springer Book Archive

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