An alternative formulation of kernel LPP with application to image recognition

被引：48

作者：

Feng, Guiyu

Hu, Dewen ^{[1
]}

Zhang, David

Zhou, Zongtan

机构：

[1] Natl Univ Def Technol, Coll Mechatron & Automat, Dept Automat Control, Changsha 410073, Hunan, Peoples R China

[2] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China

来源：

NEUROCOMPUTING | 2006年 / 69卷 / 13-15期

基金：

高等学校博士学科点专项科研基金;

关键词：

kernel principal component analysis (KPCA); locality preserving projections (LPP); kernel LPP (KLPP); image recognition;

D O I：

10.1016/j.neucom.2006.01.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Locality preserving projections (LPP) is a new subspace feature extraction method which seeks to preserve the local structure and intrinsic geometry of the data space. As the LPP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locality preserving projections (KLPP). Our algorithm consists of two steps: kernel principal component analysis (KPCA) plus LPP. We provide an outline for implementing KLPP. Experiments on the ORL face database and PolyU palmprint database demonstrate the effectiveness of the proposed algorithm. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：1733 / 1738

页数：6

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