DocumentCode :
2794627
Title :
Direct importance estimation with probabilistic principal component analyzers
Author :
Yamada, Makoto ; Sugiyama, Masashi ; Wichern, Gordon
Author_Institution :
Dept. of Comput. Sci., Tokyo Inst. of Technol., Tokyo, Japan
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
1962
Lastpage :
1965
Abstract :
The importance estimation problem (estimating the ratio of two probability density functions) has recently gathered a great deal of attention for use in various applications, e.g., outlier detection and covariate shift adaptation. In this paper, we propose a new importance estimation method using mixtures of probabilistic principal component analyzers (PPCAs). Our method employs the framework of the Kullback-Leibler importance estimation procedure (KLIEP) using using linear or kernel models. The proposed approach entitled PPCA mixture KLIEP (PM-KLIEP) can improve importance estimation accuracy with correlated and rank-deficient data. Through experiments, we show the validity of the proposed approach.
Keywords :
principal component analysis; probability; Kullback-Leibler importance estimation procedure; direct importance estimation; estimation problem; probabilistic principal component analyzers; probability density functions; Application software; Art; Computer science; Data processing; Kernel; Principal component analysis; Probability density function; State estimation; Supervised learning; Testing; EM algorithm; Importance; KLIEP; Probabilistic PCA;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495290
Filename :
5495290
Link To Document :
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