In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points. Unlike the standard pairwise correspondence between the data points, in our problem each set is partitioned into multiple clusters or classes, where the class labels define correspondences between the sets. Cluster-CCA is able to learn discriminant low dim...

We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analys...

Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and inference methods for CCA which are attract...

Hervé Abdi, Vincent Guillemot, Aida Eslami and Derek Beaton School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA Bioinformatics and Biostatistics Hub, Institut Pasteur (IP), C3BI, USR 3756 CNRS, Paris, France Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada Rotman Research Institute, Baycrest Health Sciences, Toro...

The banking structure-performance relationship has been the subject of many studies (Heggestad, 1979). This paper addresses two problems associated with previous research through analysis of the structure-performance relationship in the savings and loan association industry. One problem is that most studies estimate the structure-performance relationship with multiple regression analysis. The p...

Canonical Correlation Analysis (CCA) computes maximally-correlated linear projections of two modalities. We propose Differentiable CCA, a formulation of CCA that can be cast as a layer within a multi-view neural network. Unlike Deep CCA, an earlier extension of CCA to nonlinear projections, our formulation enables gradient flow through the computation of the CCA projection matrices, and free ch...

We discuss algorithms for performing canonical correlation analysis. In canonical correlation analysis we try to find correlations between two data sets. The canonical correlation coefficients can be calculated directly from the two data sets or from (reduced) representations such as the covariance matrices. The algorithms for both representations are based on singular value decomposition. The ...