Nonparametric Canonical Correlation Analysis

  title={Nonparametric Canonical Correlation Analysis},
  author={Tomer Michaeli and Weiran Wang and Karen Livescu},
Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear… CONTINUE READING
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