Appearance models based on kernel canonical correlation analysis

This paper introduces a new approach to constructing appearance models based on kernel canonical correlation analysis (kernel-CCA). Kernel-CCA is a non-linear extension of CCA, where a non-linear transformation of the input data is performed implicitly using kernel methods. Although, in this respect, it is similar to other generalized linear methods, kernel-CCA is especially well suited for relating two sets of measurements. The benefits of our method compared to standard feature extraction methods based on PCA will be illustrated experimentally for the task of estimating an object's pose from raw brightness images.

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