Locality preserving and global discriminant projection with prior information

  • Authors:

  • Honggang Zhang;Weihong Deng;Jun Guo;Jie Yang

  • Affiliations:

  • Beijing University of Posts and Telecommunications, School of Information and Communications Engineering, Beijing, China and Carnegie Mellon University, School of Computer Science, Pittsburgh, USA;Beijing University of Posts and Telecommunications, School of Information and Communications Engineering, Beijing, China;Beijing University of Posts and Telecommunications, School of Information and Communications Engineering, Beijing, China;Carnegie Mellon University, School of Computer Science, Pittsburgh, USA

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2010

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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.