| 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. |
| 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 |
| DOI：10.3724/SP.J.1001.2011.03843 |
| 分类号: |
| 基金项目:国家自然科学基金(60632050, 60873151, 60973098); 国家高技术研究发展计划(863)(2006AA01Z119) |
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| Optimal Discriminant Analysis Based on Kernel Extension of Graph Embedding and FaceRecognition |
|
LU Gui-Fu1,2, LIN Zhong1, JIN Zhong1
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1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;2.School of Computer Science and Information, Anhui Polytechnic University, Wuhu 241000, China
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| Abstract: |
| 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. |
| Key words: kernel extension of graph embedding optimal discriminant vector kernel principal component
analysis (KPCA) feature extraction face recognition |
