Optimizing matrix mapping with data dependent kernel for image classification

Kernel based nonlinear feature extraction is feasible to extract the feature of image for classification.The current kernel-based method endures two problems: 1) kernel-based method is to use the data vector through transforming the image matrix into vector, which will cause the store and computing burden; 2) the parameter of kernel function has the heavy influences on kernel based learning method. In order to solve the two problems, we present the method of optimizing matrix mapping with data dependent kernel for feature extraction of the image for classification. The method implements the algorithm without transforming the matrix to vector, and it adaptively optimizes the parameter of kernel for nonlinear mapping. The comprehensive experiments are implemented evaluate the performance of the algorithms.