Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
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概要
- 論文の詳細を見る
We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we emph{directly} learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated. Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.
- 一般社団法人電子情報通信学会の論文
- 2013-07-11
著者
-
Liu Song
Department Of Computer Science Tokyo Institute Of Technology
-
Sugiyama Masashi
Department of Applied Chemistry, Yamanashi University
-
QUINN John
Makerere University
-
GUTMANN Michael
University of Helsinki, Finland
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