Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15146
Title: Locally principal component learning for face representation and recognition
Authors: Yang, J
Zhang, D 
Yang, Jy
Keywords: Dimensionality reduction
Face recognition
Feature extraction
Locality-based learning
Principal component analysis (PCA)
Issue Date: 2006
Publisher: Elsevier
Source: Neurocomputing, 2006, v. 69, no. 13-15, p. 1697-1701 How to cite?
Journal: Neurocomputing 
Abstract: This paper develops a method called locally principal component analysis (LPCA) for data representation. LPCA is a linear and unsupervised subspace-learning technique, which focuses on the data points within local neighborhoods and seeks to discover the local structure of data. This local structure may contain useful information for discrimination. LPCA is tested and evaluated using the AT&T face database. The experimental results show that LPCA is effective for dimension reduction and more powerful than PCA for face recognition.
URI: http://hdl.handle.net/10397/15146
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2006.01.009
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