This paper provides an overview of Kernel Canonical Correlation Analysis. KCCA is a technique for finding common semantic features between different views of data. Applications on text retrieval, categorization and image retrieval based on text queries are presented.

We consider several stochastic process methods for performing canonical correlation analysis (CCA). The first uses a Gaussian Process formulation of regression in which we use the current projection of one data set as the target for the other and then repeat in the opposite direction. The second uses a method which relies on probabilistically sphering the data, concatenating the two streams and...

Canonical Correlation and Assortative Matching: A Remark In the context of the Beckerian theory of marriage, when men and women match on a singledimensional index that is the weighted sum of their respective multivariate attributes, many papers in the literature have used linear canonical correlation, and related techniques, in order to estimate these weights. We argue that this estimation tech...

Dimension reduction is helpful and often necessary in exploring nonlinear or nonparametric regression structures with a large number of predictors. We consider using the canonical variables from the design space whose correlations with a spline basis in the response space are significant. The method can be viewed as a variant of sliced inverse regression (SIR) with simple slicing replaced by Bs...

Canonical correlation analysis studies associations between two sets of random variables. Its standard computation is based on sample covariance matrices, which are however very sensitive to outlying observations. In this note we introduce, discuss and compare four different ways for performing a robust canonical correlation analysis. One method uses robust estimators of the involved covariance...

Canonical correlation analysis is a widely used multivariate statistical technique for exploring the relation between two sets of variables. This paper considers the problem of estimating the leading canonical correlation directions in high dimensional settings. Recently, under the assumption that the leading canonical correlation directions are sparse, various procedures have been proposed for...

This paper shows how canonical correlation can be used to learn a detector for corner orientation invariant to corner angle and intensity. Pairs of images with the same corner orientation but different angle and intensity are used as training samples. Three different image representations; intensity values, products between intensity values, and local orientation are examined. The last represen...

We review a new method of performing Canonical Correlation Analysis (CCA) with Artificial Neural Networks. We have previously [4, 3] compared its capabilities with standard statistical methods on simple data sets where the maximum correlations are given by linear filters. In this paper, we re-derive the learning rules from a probabilistic perspective and then by use of a specific prior on the w...

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of variables. RGCCA is a component-based approach which aims to study the relationships between several sets of variables. The quality and interpretability of the RGCCA components are likely to be affected by the usefulness and relevance of the varia...

We review [4] a new method of performing Canonical Correlation Analysis with Artificial Neural Networks. We demonstrate its capability on a real data set where the results are compared with those achieved with standard statistical tools. In this paper, we extend the method by implementing a very precise set of constraints which allow multiple correlations to be found at once. We demonstrate the...