We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification. Individual canonical correlation trees are binary decision trees with hyperplane splits based on canonical correlation components. Unlike axisaligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the input features and therefore more naturally r...

corporate governance is the system by which corporations are directed and controlled. corporate governance also provides the structure through which company objectives are established, the means to achieve these objectives, and how to monitor their performance. icts have the potential to make organization and its services more transparent by providing easy access to information. this research a...

A rigorous derivation is provided for canonical correlations and partial canonical correlations for certain Hilbert space indexed stochastic processes. The formulation relies on a key congruence mapping between the space spanned by a second order, H-valued, process and a particular Hilbert function space deriving from the process’ covariance operator. The main results are obtained via an applic...

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 is a versatile multivarite technique that is prone to distortion as a result of correlation outliers. The detection and treatment of such outliers is complicated by outlier masking effects. Methods that check the effect of one observation at a time are therefore unsuccessful as diagnostic tools. In this paper we suggest that an approach involving the robust estima...

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...