Skip to main content
SpringerLink
Log in

Non-parametric e-mixture of Density Functions

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

Included in the following conference series:

Abstract

Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m- and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Tu, W., Sun, S.: A subject transfer framework for EEG classification. Neurocomputing 82, 109–116 (2011)

    Article  Google Scholar 

  2. Silva, J., Narayanan, S.S.: Information divergence estimation based on data-dependent partitions. In: Proceedings on IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 429–432 (2001)

    Google Scholar 

  3. Amari, S., Nagaoka, H.: Methods of Information Geometry. American Mathematical Society, Providence (2000)

    MATH  Google Scholar 

  4. McLachlan, G., Peel, D.: Finite Mixture Models. Probability and Statistics. Wiley, New York (2000)

    Book  MATH  Google Scholar 

  5. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 39, 1–38 (1997)

    MathSciNet  MATH  Google Scholar 

  6. Genest, C., Zidek, J.V.: Combining probability distributions: a critique and an annotated bibliography. Stat. Sci. 1, 114–135 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  7. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  8. Murata, N., Fujimoto, Y.: Bregman divergence and density integration. J. Math Ind. 1, 97–104 (2009)

    MathSciNet  MATH  Google Scholar 

  9. Wang, Q., Kulkarni, S.R., Verdú, S.: Divergence estimation of continuous distributions based on data-dependent partitions. IEEE Trans. Inf. Theor. 51, 3064–3074 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  10. Wang, Q., Kulkarni, S.R., Verdú, S.: Divergence estimation for multidimensional densities via k-nearest-neighbor distances. IEEE Trans. Inf. Theor. 55, 2392–2405 (2009)

    Article  MathSciNet  Google Scholar 

  11. Hino, H., Murata, N.: Information estimators for weighted observations. Neural Netw. 1, 260–275 (2013)

    Article  MATH  Google Scholar 

Download references

Acknowledgement

Part of this work was supported by JSPS KAKENHI No. 25120009, 25120011, and 16K16108.

Author information

Authors and Affiliations

  1. University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Hideitsu Hino

  2. Waseda University, Okubo 3–4–1, Shinjuku-ku, Tokyo, 169–8555, Japan

    Ken Takano & Noboru Murata

  3. AIST, 1-1-1 Umezono, Tsukuba, Ibaraki, 305–8568, Japan

    Shotaro Akaho

Authors
  1. Hideitsu Hino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ken Takano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shotaro Akaho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noboru Murata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hideitsu Hino .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Hino, H., Takano, K., Akaho, S., Murata, N. (2016). Non-parametric e-mixture of Density Functions. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46672-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation