Outlier resistant estimators for canonical correlation analysis

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 different ways for performing a robust canonical correlation analysis. Two methods are based on robust estimators of covariance matrices, the others on projection-pursuit techniques.