Multiview Learning with Labels

CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by extension CCA, does not use label information. Fisher Linear Discriminant Analysis uses label information to find informative projections, which results. We show that LDA and its dual can both be formulated as generalized eigenproblems, enabling a kernel formulation. We derive a regularised two-view equivalent of Fisher Linear Discriminant (LDA-2) and its corresponding dual (LDA-2K), both of which can also be formulated as generalized eigenproblems.