摘要
将压缩映射和同构映射引入核化图嵌入框架(kernel extension of graph embedding,简称KGE),从理论上证明了KGE框架内的各种核算法其实质是KPCA(kernel principal component analysis)+LGE(linear extension of graph embedding,简称LGE)框架内的线性降维算法,并且基于所给出的理论框架提出了一种综合利用零空间和非零空间鉴别信息的组合方法.任何一种可以用核化图嵌入框架描述的核算法,都可以有相应的组合方法.在ORL,Yale,FERET和PIE人脸数据库上验证了所提出的理论和方法的有效性.
By making use of compressive mapping and isomorphic mapping in the kernel extension of graph embedding, this paper proves that the essence of kernel extension of graph embedding （KGE） is KPCA （kernel principal component analysis） plus all kinds of linear dimension reduction approaches interpreted in a linear extension of graph embedding （LGE）. Based on the theory framework, a combined framework, which takes advantage of the discriminant feature in both null and non-null spaces, is developed. Furthermore, every kernel dimensionality reduction algorithm has its own corresponding combined algorithm. The experimental results from ORL, Yale, FERET and PIE face databases show that the proposed methods are better than the original methods in terms of recognition rate.
出处
《软件学报》
EI
CSCD
北大核心
2011年第7期1561-1570,共10页
Journal of Software
基金
国家自然科学基金(60632050
60873151
60973098)
国家高技术研究发展计划(863)(2006AA01Z119)
关键词
核化图嵌入
最优鉴别矢量
核主成分分析
特征抽取
人脸识别
kernel extension of graph embedding
optimal discriminant vector
kernel principal componentanalysis （KPCA）
feature extraction
face recognition
