期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2014
卷号：9
期号：10
页码：137-144
出版社：SERSC
摘要：According to the error approximation problem the sparse preserving projections (SPP) reconstruct the original sample. This paper proposes the algorithms based on orthogonal sparse preserving projections of kernel. In order to get sparse representation coefficients that contain more identification information by kernel method, it mapped samples to high- dimensional feature space to. Then, reconstructing sparse coefficient of kernel sparse representation increase the similar non neighbor sample weight, and reduce heterogeneous neighbor sample weight. Finally, the whole orthogonal constraint transformation improve the ability of sparse retain sample. The algorithm experiments were carried out on the YALE_B and ORL face database, and the recognition rate reached 96.3%, and the results verify the effectiveness and robustness of the algorithm.
