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...

Regularization Methods for Canonical Correlation Analysis, Rank Correlation Matrices and Renyi Correlation Matrices by Ying Xu Doctor of Philosophy in Statistics University of California, Berkeley Professor Peter J. Bickel, Chair In multivariate analysis, canonical correlation analysis is a method that enable us to gain insight into the relationships between the two sets of variables. It determ...

We review the recently proposed method of Relevance Vector Machines which is a supervised training method related to Support Vector Machines. We also review the statistical technique of Canonical Correlation Analysis and its implementation in a Feature Space. We show how the technique of Relevance Vectors may be applied to the method of Kernel Canonical Correlation Analysis to gain a very spars...

In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-spars...

Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables...