Discriminative Locality Preserving Canonical Correlation Analysis

  • Xiang Zhang
  • Naiyang Guan
  • Zhigang Luo
  • Long Lan
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


In this paper, we propose a novel dimension reduction method based on canonical correlation analysis, called discriminative locality preserving canonical correlation analysis (DLPCCA) method. In particular, we use discriminative information to maximize the correlations between intra-class samples, and maximize the margins between inter-class samples. Moreover, local preserving data structure can be used to estimate the data structure, and thus DLPCCA achieves better performance. Experimental results on Yale and ORL datasets show that DLPCCA outperforms the representative algorithms including CCA, KCCA, LPCCA and LDCCA.


Canonical Correlation Analysis Discriminant Analysis Geometric Structure 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiang Zhang
    • 1
  • Naiyang Guan
    • 1
  • Zhigang Luo
    • 1
  • Long Lan
    • 1
  1. 1.School of Computer ScienceNational University of Defense TechnologyChangshaChina

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