哈尔滨工程大学学报2011，Vol.32Issue(7)：938-942,5.DOI:10.3969/j.issn.1006-7043.2011.07.018
核空间正交及不相关邻域保持鉴别嵌入算法
Kernel orthogonal and uncorrelated neighborhood preservation discriminant embedding algorithm
摘要
Abstract
In view of the problems of nonlinear feature extraction in face recognition, a new algorithm of orthogonal optimal discriminant vectors and a new algorithm of statistically uncorrelated optimal discriminant vectors in a kernel space were proposed based on neighborhood preservation embedding ( NPE). First, nonlinear kernel mapping was used to map the face data into an implicit feature space. Then the algorithm maximized inter-class neighborhood scatter information while minimizing intra-class neighborhood scatter information in the kernel space, which helped to improve its discriminant ability. Finally, the kernel orthogonal preserving discriminant embedding (KONPDE) algorithm and the kernel uncorrelated neighborhood preserving, discriminant embedding ( KUNPDE) algorithm were obtained by constraining the base vectors orthogonal and uncorrelated respectively. Also the general theorem for solving the base vectors of the above two algorithms and the derivations of the algorithms were specifically introduced. Experiments on Yale and PIE demonstrate the effectiveness of the algorithms, and show that these algorithms can reduce the dimensions of the data and improve the discriminant ability.关键词
流形学习/人脸识别/嵌入算法/核空间Key words
manifold learning/face recognition/embedding algorithm/kernel space分类
能源科技引用本文复制引用
刘冠群,王庆军,张汝波,潘海为..核空间正交及不相关邻域保持鉴别嵌入算法[J].哈尔滨工程大学学报,2011,32(7):938-942,5.基金项目
国家863计划资助项目(2009AA04Z215) （2009AA04Z215）
国家自然科学基金资助项目(60803036,60975071). （60803036,60975071）
