Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28157
Title: Supervised optimal locality preserving projection
Authors: Wong, WK 
Zhao, HT
Keywords: Classification
Dimensionality reduction
Feature extraction
Manifold learning
Issue Date: 2012
Publisher: Elsevier
Source: Pattern recognition, 2012, v. 45, no. 1, p. 186-197 How to cite?
Journal: Pattern recognition 
Abstract: In the past few years, the computer vision and pattern recognition community has witnessed a rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among these methods, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Unlike the unsupervised learning scheme of LPP, this paper follows the supervised learning scheme, i.e. it uses both local information and class information to model the similarity of the data. Based on novel similarity, we propose two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal locality preserving projection (NL-SOLPP). Optimal here means that the extracted features via SOLPP (or NL-SOLPP) are statistically uncorrelated and orthogonal. We compare the proposed SOLPP and NL-SOLPP with LPP, orthogonal locality preserving projection (OLPP) and uncorrelated locality preserving projection (ULPP) on publicly available data sets. Experimental results show that the proposed SOLPP and NL-SOLPP achieve much higher recognition accuracy.
URI: http://hdl.handle.net/10397/28157
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2011.05.014
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