Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis (情報論的学習理論と機械学習)
スポンサーリンク
概要
- 論文の詳細を見る
Methods for estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and conditional probability estimation. In this paper, we propose a new density-ratio estimator which incorporates dimensionality reduction into the density-ratio estimation procedure. Through experiments, the proposed method is shown to compare favorably with existing density-ratio estimators in terms of both accuracy and computational costs.
- 2011-06-13
著者
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Yamada Makoto
Department Of Chemistry Faculty Of Science Okayama University Of Science
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Yamada Makoto
Department Of Chemistry And Biomolecular Science Toho University
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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