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International Conference on Neural Information Processing

ICONIP 2012: Neural Information Processing pp 594–602Cite as

Robust Active Learning for Linear Regression via Density Power Divergence

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7665)

Abstract

The performance of active learning (AL) is crucially influenced by the existence of outliers in input samples. In this paper, we propose a robust pool-based AL measure based on the density power divergence. It is known that the density power divergence can be accurately estimated even under the existence of outliers within data. We further derive an AL scheme based on an asymptotic statistical analysis on the M-estimator. The performance of the proposed framework is investigated empirically using artificial and real-world data.

Keywords

  • Active Learning
  • Density Power Divergence
  • Regression

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  • DOI: 10.1007/978-3-642-34487-9_72
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Author information

Authors and Affiliations

  1. ISIR, Osaka University, 8-1, Mihogaoka, Ibaraki, Osaka, Japan

    Yasuhiro Sogawa, Yoshinobu Kawahara & Takashi Washio

  2. Japan Science and Technology Agency, 1-4-14, Shibata, Kita-ku, Osaka, Japan

    Tsuyoshi Ueno

Authors
  1. Yasuhiro Sogawa
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  2. Tsuyoshi Ueno
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  3. Yoshinobu Kawahara
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  4. Takashi Washio
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Sogawa, Y., Ueno, T., Kawahara, Y., Washio, T. (2012). Robust Active Learning for Linear Regression via Density Power Divergence. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_72

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  • DOI: https://doi.org/10.1007/978-3-642-34487-9_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34486-2

  • Online ISBN: 978-3-642-34487-9

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