Cone-restricted kernel subspace methods

We propose cone-restricted kernel subspace methods for pattern classification. A cone is mathematically defined in a manner similar to a linear subspace with a nonnegativity constraint. Since the angles between vectors (i.e., inner products) are fundamental to the cone, kernel tricks can be directly applied. The proposed methods approximate the distribution of sample patterns by using the cone in kernel feature space via kernel tricks, and the classification is more accurate than that of the kernel subspace method. Due to the nonlinearity of kernel functions, even a single cone in the kernel feature space can can cope with multi-modal distributions in the original input space. In the experimental results on person detection and motion detection, the proposed methods exhibit the favorable performances.