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...