Locality preserving and global discriminant projection with prior information |
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Authors: | Honggang Zhang Weihong Deng Jun Guo Jie Yang |
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Affiliation: | (1) State Key Laboratory of Software Engineering, Wuhan University, 430072 Wuhan, China;(2) College of Computer Science and Technology, Wuhan University of Science and Technology, 430081 Wuhan, China;(3) Key Lab Complex System & Intelligence Science, Institute of Automation, Chinese Academy of Science, 100190 Beijing, China; |
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Abstract: | Existing supervised and semi-supervised dimensionality reduction methods utilize training data only with class labels being
associated to the data samples for classification. In this paper, we present a new algorithm called locality preserving and
global discriminant projection with prior information (LPGDP) for dimensionality reduction and classification, by considering
both the manifold structure and the prior information, where the prior information includes not only the class label but also
the misclassification of marginal samples. In the LPGDP algorithm, the overlap among the class-specific manifolds is discriminated
by a global class graph, and a locality preserving criterion is employed to obtain the projections that best preserve the
within-class local structures. The feasibility of the LPGDP algorithm has been evaluated in face recognition, object categorization
and handwritten Chinese character recognition experiments. Experiment results show the superior performance of data modeling
and classification to other techniques, such as linear discriminant analysis, locality preserving projection, discriminant
locality preserving projection and marginal Fisher analysis. |
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Keywords: | |
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