Deep Canonical Correlation Analysis

We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn com-plex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analy-sis (CCA). It is an alternative to the nonpara-metric method kernel canonical correlation analysis (KCCA) for learning correlated non-linear transformations. Unlike KCCA, DCCA does not require an inner product, and has the advantages of a parametric method: train-ing time scales well with data size and the training data…