计算机科学与探索2017，Vol.11Issue(2)：286-293,8.DOI:10.3778/j.issn.1673-9418.1512064
高效求解方法的核典型相关分析算法
Kernel Canonical Correlation Analysis Based on Solving Method with High Efficiency
摘要
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-St) 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-St can effectively reduce the computation time without affecting the recognition rate.关键词
核化图/典型相关分析/降维处理/散度矩阵Key words
kernel extension of graph/canonical correlation analysis/dimension reducing processing/scatter matrix分类
信息技术与安全科学引用本文复制引用
林克正,王海燕,李骜,荣友湖..高效求解方法的核典型相关分析算法[J].计算机科学与探索,2017,11(2):286-293,8.基金项目
The National Natural Science Foundation of China under Grant No.61501147 (国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Heilongjiang Province under Grant No.F2015040(黑龙江省自然科学基金). （黑龙江省自然科学基金）
