Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates

12 May 2016Zhuang MaXiaodong Li

Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables. In this paper, motivated by recent success of applying CCA to learn low dimensional representations of high dimensional objects, we propose to quantify the estimation loss of CCA by the excess prediction loss defined through a prediction-after-dimension-reduction framework... (read more)

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