Gaussian processes for canonical correlation analysis

We consider several stochastic process methods for performing canonical correlation analysis (CCA). The first uses a Gaussian process formulation of regression in which we use the current projection of one data set as the target for the other and then repeat with the second projection as the target for adapting the parameters of the first. The second uses a method which relies on probabilistically sphering the data, concatenating the two streams and then performing a probabilistic PCA. The third gets the canonical correlation projections directly without having to calculate the filters first. We also investigate the use of nonlinearity and a method for sparsification of these algorithms.