Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Mixture of Probabilistic Factor Analysis Model and Its Applications

  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 2001 (ICANN 2001)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 189.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. B.D.O.Anderson, B.D.O., Moore, J.B.: Optimal Filtering Prentice-Hall (1979)

    Google Scholar 

  2. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society B39-1 (1977) 1–38

    MathSciNet  Google Scholar 

  3. Rumelhart, D.E. Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. in Rumelhart, D.E. et al.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. 1, The MIT Press (1986) 321–362

    Google Scholar 

  4. Tanaka, M.: Modeling of mixtures of principal component analysis model with genetic algorithm. Proc. 31st ISCIE International Symposium on Stochastic Systems Theory and Its Applications (2000) 157–162

    Google Scholar 

  5. Tipping, M.E., Bishop, C.M.: Mixtures of Probabilistic Principal Component Analysers. Technical Report NCRG/97/003/, Aston University, UK (1998) 29 pages

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation