Independent component analysis by direct density-ratio estimation (ニューロコンピューティング)

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概要

• 論文の詳細を見る

• Accurately evaluating statistical independence among random variables is a key component of Independent Component Analysis (ICA). In this paper, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method. Our basic idea is to estimate the ratio of probability densities directly without going through density estimation, by which a hard task of density estimation can be avoided. In this density-ratio approach, a natural cross-validation procedure is available for model selection. Thanks to this, all tuning parameters such as the kernel width or the regularization parameter can be objectively optimized. This is a highly useful property in unsupervised learning problems such as ICA. Based on this novel independence measure, we develop a new ICA algorithm named Least-squares Independent Component Analysis (LICA).

• 社団法人電子情報通信学会の論文
• 2009-03-04

著者

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• Suzuki Taiji

  Department Of Mathematical Informatics Graduate School Of Information Science And Technology Univers

• Suzuki Taiji

  University of Tokyo

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

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