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IEICE Transactions on Information and Systems
Vol. E93.D (2010) No. 10 P 2846-2849

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http://doi.org/10.1587/transinf.E93.D.2846

Regular Section

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.

Copyright © 2010 The Institute of Electronics, Information and Communication Engineers

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