关键词: 核化图, 典型相关分析, 降维处理, 散度矩阵

Abstract: Aiming at the problem of the complexity of high dimensional small sample data during the process of KGE (kernel extension of graph embedding), by a fast calculation model based on KGE, this paper proposes a new KGE/CCA algorithm (KGE/CCA-S_t) which can reduce the computational complexity of kernel matrix. Firstly, sample data are projected into corresponding rank space of total scatter matrix in which the dimension is far lower than that in original sample space. Then, kernel canonical correlation analysis is used for feature extraction, the calculation of kernel matrix is decreased in this process. Through the simulation experiments on Yale face database and JAFFE face database, the results show that the recognition rate of the KGE/CCA algorithms is significantly better than that of KFD, KLPP and KNPE algorithms. Compared with the traditional KGE/CCA algorithm, KGE/CCA-S_t can effectively reduce the computation time without affecting the recognition rate.

Key words: kernel extension of graph, canonical correlation analysis, dimension reducing processing, scatter matrix