Robust active learning for linear regression via density power divergence

Authors:
Yasuhiro Sogawa;Tsuyoshi Ueno;Yoshinobu Kawahara;Takashi Washio
Affiliations:
ISIR, Osaka University, Ibaraki, Osaka, Japan;Japan Science and Technology Agency, Kita-ku, Osaka, Japan;ISIR, Osaka University, Ibaraki, Osaka, Japan;ISIR, Osaka University, Ibaraki, Osaka, Japan
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

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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.