Canonical Dependency Analysis based on Squared-loss Mutual Information
スポンサーリンク
概要
- 論文の詳細を見る
Canonical correlation analysis (CCA) is a classical technique to iteratively find projection directions for two sets of variables such that their correlation is maximized. In this paper, we propose an extension of CCA based on a squared-loss variant of mutual information. The proposed method, which we call least-squares canonical dependency analysis (LSCDA), has various useful properties, for example, it can capture higher-order correlations, it can simultaneously find multiple projection directions (i.e., subspaces), it does not involve density estimation, and it is equipped with a model selection strategy. We illustrate the usefulness of LSCDA through experiments.
- 2011-08-29
著者
-
Masashi Sugiyama
Department Of Computer Science Tokyo Institute Of Technology
-
Masayuki Karasuyama
Department of Computer Science, Tokyo Institute of Technology
-
Masayuki Karasuyama
Department Of Computer Science Tokyo Institute Of Technology
関連論文
- Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
- Artist Agent A2: Stroke Painterly Rendering Based on Reinforcement Learning
- Canonical Dependency Analysis based on Squared-loss Mutual Information
- Output Divergence Criterion for Active Learning in Collaborative Settings