Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel o...

Given a bivariate distribution, the set of canonical correlations and functions is in general finite or countable. By using an inner product between two functions via an extension of the covariance, we find all the canonical correlations and functions for the so-called Cuadras-Augé copula and prove the continuous dimensionality of this distribution.

This paper describes some new approaches for the feature extraction of SSVEP signal in EEG signal processing. One of them is Canonical Correlation Analysis (CCA) and another one is CWT along with ANN. Basically CCA is applied to analyze the frequency components of SSVEP in EEG. The essence of this method is to extract narrowband frequency components of SSVEP in EEG. The CWT offers a valuable to...

in order to have a successful breeding program, it is important to determine the relationship among the traits. this study was conducted at research farm of isfahan university of technology to evaluate relationships among some of the agronomic and physiological traits and grain yield of ten bread wheat cultivars in optimum and stress moisture (irrigation after 70±3 and 130±3 mm evaporation from...

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...