Neighborhood preserving sparse representation based on Nyström method for image set classification on symmetric positive definite matrices

Abstract

In the field of pattern recognition, using the symmetric positive-definite matrices to represent image set has been widely studied, and sparse representation-based classification algorithm on the symmetric positive-definite matrix manifold has attracted great attention in recent years. However, the existing kernel representation-based classification methods usually use kernel trick with implicit kernel to rewrite the optimization function and will have some problems. To address the problem, a neighborhood preserving explicit kernel representation-based classification-based Nyström method is proposed on symmetric positive-definite manifold by embedding the symmetric positive-definite matrices into a Reproducing Kernel Hilbert Space with an explicit kernel based on Nyström method. Thus, we can take full advantage of kernel space characteristics. Through the experimental results, we demonstrate the better performance of our method in the task of image set classification.