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...

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...

A classical problem in statistics is to study relationships between several blocks of variables. The goal is to find variables of one block directly related to variables of other blocks. The Regularized Generalized Canonical Correlation Analysis (RGCCA) is a very attractive framework to study such a kind of relationships between blocks. However, RGCCA captures linear relations between blocks an...

We present Deep Generalized Canonical Correlation Analysis (DGCCA) – a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst,...

We present a novel transfer learning approach to cross-camera action recognition. Inspired by canonical correlation analysis (CCA), we first extract the spatio-temporal visual words from videos captured at different views, and derive a correlation subspace as a joint representation for different bag-of-words models at different views. Different from prior CCA-based approaches which simply train...

The purpose of this article is to reduce potential statistical barriers and open doors to canonical correlation analysis (CCA) for applied behavioral scientists and personality researchers. CCA was selected for discussion, as it represents the highest level of the general linear model (GLM) and can be rather easily conceptualized as a method closely linked with the more widely understood Pearso...

A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial rel...

A method of reconstructing perceived or imagined music by analyzing brain activity has not yet been established. As a first step toward developing such a method, we aimed to reconstruct the imagery of rhythm, which is one element of music. It has been reported that a periodic electroencephalogram (EEG) response is elicited while a human imagines a binary or ternary meter on a musical beat. Howe...

This analysis was conducted to explore the association between 5 birth size measurements (weight, length and head, chest and mid-upper arm [MUAC] circumferences) as dependent variables and 10 maternal factors as independent variables using canonical correlation analysis (CCA). CCA considers simultaneously sets of dependent and independent variables and, thus, generates a substantially reduced t...