Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25032
Title: An alternative formulation of kernel LPP with application to image recognition
Authors: Feng, G
Hu, D
Zhang, D 
Zhou, Z
Keywords: Image recognition
Kernel LPP (KLPP)
Kernel principal component analysis (KPCA)
Locality preserving projections (LPP)
Issue Date: 2006
Publisher: Elsevier
Source: Neurocomputing, 2006, v. 69, no. 13-15, p. 1733-1738 How to cite?
Journal: Neurocomputing 
Abstract: 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.
URI: http://hdl.handle.net/10397/25032
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2006.01.006
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