2000 | OriginalPaper | Buchkapitel
Robust Methods for Canonical Correlation Analysis
verfasst von : Catherine Dehon, Peter Filzmoser, Christophe Croux
Erschienen in: Data Analysis, Classification, and Related Methods
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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 matrices, another one uses the signs of the observations, a third approach is based on projection pursuit, and finally an alternating regression algorithm for canonical analysis is proposed.