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Robust estimation of canonical correlation coefficients
Digital imaging in media and education : 28th workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR), 2004,
Canonical Correlation Analysis is well suited for regression tasks in appearance-based approach to modelling of objects and scenes. However, since it relies on the standard projection it is inherently non-robust. In this paper we propose to embed the estimation of CCA coefficients in an augmented PCA space, which enables detection of outliers and preserves regression-relevant information enabling robust estimation of canonical correlation coefficients.