Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27369
Title: A multi-manifold discriminant analysis method for image feature extraction
Authors: Yang, W
Sun, C
Zhang, L 
Keywords: Feature extraction
Image recognition
LDA
Multi-manifold learning
Issue Date: 2011
Publisher: Elsevier
Source: Pattern recognition, 2011, v. 44, no. 8, p. 1649-1657 How to cite?
Journal: Pattern recognition 
Abstract: In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance.
URI: http://hdl.handle.net/10397/27369
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2011.01.019
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