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...

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 extend multi-way, multivariate ANOVA-type analysis to cases where one covariate is the view, with features of each view coming from different, highdimensional domains. The different views are assumed to be connected by having paired samples; this is common in our main application, biological experiments integrating data from different sources. Such experiments typically also include a contro...

We have recently developed several ways of performing Canonical Correlation Analysis [1, 5, 7, 4] with probabilistic methods rather than the standard statistical tools. However, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artificial neural network methods [3, 2]. In this paper, we examine a rece...

We derive a new method of performing Canonical Correlation Analysis with Artiicial Neural Networks. We demonstrate its capability on a simple artiicial data set and then on a real data set where the results are compared with those achieved with standard statistical tools. We then extend the method to deal with a situation where there are two equal competing correlations within the datasets and ...

We consider two 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 Dirichlet process of Gaussian models where the Gaussian models are determined by Probabilistic CC...

CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with a...