  Canonical correlation analysis is a technique to extract common features from
a pair of multivariate data. In complex situations, however, it does not
extract useful features because of its linearity. On the other hand, kernel
method used in support vector machine is an efficient approach to improve such
a linear method. In this paper, we investigate the effectiveness of applying
kernel method to canonical correlation analysis.

Machine Learning;Computer Vision and Pattern Recognition