Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-t...Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-tion problem. SVMs are classifiers which have demonstrated high generalization capabilities. Kernel PCA is a featureextraction technique which is proposed as a nonlinear extension of a PCA. We illustrate the potential of SVMs andkernel PCA on the Yale database and compare with a PCA based algorithm. The experiments indicate that SVMs andkernel PCA are superior to the PCA method.展开更多
文摘Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-tion problem. SVMs are classifiers which have demonstrated high generalization capabilities. Kernel PCA is a featureextraction technique which is proposed as a nonlinear extension of a PCA. We illustrate the potential of SVMs andkernel PCA on the Yale database and compare with a PCA based algorithm. The experiments indicate that SVMs andkernel PCA are superior to the PCA method.
