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Density Ratio Estimation: A New Versatile Tool for Machine Learning

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Advances in Machine Learning (ACML 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5828))

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Abstract

A new general framework of statistical data processing based on the ratio of probability densities has been proposed recently and gathers a great deal of attention in the machine learning and data mining communities [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. This density ratio framework includes various statistical data processing tasks such as non-stationarity adaptation [18,1,2,4,13], outlier detection [19,20,21,6], and conditional density estimation [22,23,24,15]. Furthermore, mutual information—which plays a central role in information theory [25]—can also be estimated via density ratio estimation. Since mutual information is a measure of statistical independence between random variables [26,27,28], density ratio estimation can be used also for variable selection [29,7,11], dimensionality reduction [30,16], and independent component analysis [31,12].

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Author information

Authors and Affiliations

  1. Department of Computer Science, Tokyo Institute of Technology,  

    Masashi Sugiyama

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  1. Masashi Sugiyama
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Editors and Affiliations

  1. National Laboratory for Novel Software Technology, Nanjing University, 22 Hankou Road, 210093, Nanjing, China

    Zhi-Hua Zhou

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 567, Osaka, Ibaraki, Japan

    Takashi Washio

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Sugiyama, M. (2009). Density Ratio Estimation: A New Versatile Tool for Machine Learning. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-05224-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05223-1

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