Robust estimation of canonical correlation coefficients

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.