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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))

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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