Presentation 2014-11-17
Online Direct Density-ratio Estimation under the Kullback-Leibler Loss
PLESSIS Marthinus Christoffel DU, Hiroaki SHIINO, Masashi SUGIYAMA,
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Abstract(in English) Many machine learning problems, such as non-stationarity adaptation, outlier detection, dimensionality reduction, and conditional density estimation, can be effectively solved by using the ratio of probability densities. Since the naive two step procedure of first estimating the probability densities and then taking their ratio performs poorly, methods to directly estimate the density ratio from two sets of samples without density estimation have been extensively studied recently. However, these methods are batch algorithms that use the whole dataset to estimate the density ratio, and they are inefficient in the online setup where training samples are provided sequentially and solutions are updated incrementally without storing previous samples. In this paper, we propose an online version of a density ratio estimator based on the adaptive regularization of weight vectors (AROW). Through experiments on inlier-based outlier detection, we demonstrate the usefulness of the proposed method.
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Keyword(in English) online learning / density-ratio estimation / adaptive regularization of weight vectors / outlier detection
Paper # IBISML2014-59
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Committee IBISML
Conference Date 2014/11/10(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Online Direct Density-ratio Estimation under the Kullback-Leibler Loss
Sub Title (in English)
Keyword(1) online learning
Keyword(2) density-ratio estimation
Keyword(3) adaptive regularization of weight vectors
Keyword(4) outlier detection
1st Author's Name PLESSIS Marthinus Christoffel DU
1st Author's Affiliation Department of Complexity Science and Engineering, University of Tokyo()
2nd Author's Name Hiroaki SHIINO
2nd Author's Affiliation Department of Computer Science, Tokyo Institute of Technology
3rd Author's Name Masashi SUGIYAMA
3rd Author's Affiliation Department of Complexity Science and Engineering, University of Tokyo
Date 2014-11-17
Paper # IBISML2014-59
Volume (vol) vol.114
Number (no) 306
Page pp.pp.-
#Pages 6
Date of Issue