Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
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概要
- 論文の詳細を見る
Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.
- （社）電子情報通信学会の論文
- 2010-10-01
著者
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Wichern Gordon
Mit Lincoln Laboratory
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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SIMM JAAK
Tokyo Institute of Technology
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Simm Jaak
Department Of Computer Science Tokyo Institute Of Technology
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Wichern Gordon
Mit Lincoln Lab.
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YAMADA Makoto
Tokyo Institute of Technology
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Yamada Makoto
Tokyo Inst. Of Technol.
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