计算机应用研究2012，Vol.29Issue(12)：4758-4760,3.DOI:10.3969/j.issn.1001-3695.2012.12.093
一种核化图嵌入算法的快速求解模型
Fast model for kernel extension of graph embedding
摘要
Abstract
Kernel extension of graph embedding for the small sample size problem such as face recognition needs not only a lot of computation time but also very large memory cost, then this paper presented a fast model for kernel extension of graph embedding. Firstly, it reduced the original samples into a lower space, which was feasible according to theorem 1 and theorem 2. Two theorems also show that this dimension reduction process is not losing any discriminant information. Then it computed the new low dimensional samples by kernel extension of graph embedding. The numerical experiments on facial database show that the proposed model not only reduce the computational time but also ensure rate of recognition in classification.关键词
核化图嵌入算法/小样本问题/模型/鉴别信息/分类Key words
kernel extension of graph embedding/ small sample size problem/ model/ discriminant information/ classification分类
信息技术与安全科学引用本文复制引用
林玉娥,李敬兆,梁兴柱,林玉荣..一种核化图嵌入算法的快速求解模型[J].计算机应用研究,2012,29(12):4758-4760,3.基金项目
国家自然科学基金资助项目(60975009,61170060) （60975009,61170060）
安徽省自然科学基金资助项目(1208085QF123,11040606M135) （1208085QF123,11040606M135）
安徽省高等学校自然科学基金资助项目(KJ2012Z084,KJ2011A083) （KJ2012Z084,KJ2011A083）
