Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections them. Several variants CCA have been introduced in the literature, particular, based on deep neural networks learning highly nonlinear transformations views. As these models are parameterized conventionally, their learnable parameters remain independent inputs ...

Given two data matrices X and Y , Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Y v. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. To this end, we propose a novel Sparse weighted cano...

Canonical Correlation is one of the most general of the multivariate techniques. It is used to investigate the overall correlation between two sets of variables (p’ and q’). The basic principle behind canonical correlation is determining how much variance in one set of variables is accounted for by the other set along one or more axes. If there is more than one axis, they must be orthogonal. Un...