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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)

Abstract

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.

Keywords

Canonical Correlation Analysis Discriminant Analysis Geometric Structure 

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References

  1. 1.
    Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)zbMATHGoogle Scholar
  2. 2.
    Bach, F., Jordan, M.I.: A Probabilistic Interpretation of Canonical Correlation Analysis. Technical Report 688 (2005)Google Scholar
  3. 3.
    Zheng, W., Zhou, X., Zou, C., et al.: Facial expression recognition using kernel canonical correlation analysis. IEEE Trans. Neural Network 17, 233–238 (2006)CrossRefGoogle Scholar
  4. 4.
    Nielsen, A.: Multi-set canonical correlation analysis and multispectral truly multi-temporal remote sensing data, image processing. IEEE Trans. Image Processing 11, 293–305 (2002)CrossRefGoogle Scholar
  5. 5.
    Hardoon, D., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Computation 16, 2639–2664 (2004)zbMATHCrossRefGoogle Scholar
  6. 6.
    Horikawa, Y.: Use of autocorrelation kernels in kernel canonical correlation analysis for text classification. In: International Conference on Neural Information Processing, pp. 1235–1240 (2004)Google Scholar
  7. 7.
    Li, Y., Shawe-Taylor, J.: Using KCCA for Japanese–English cross-language information retrieval and document classification. Journal of Intelligent Information Systems 27, 117–133 (2006)CrossRefGoogle Scholar
  8. 8.
    Sun, L., Ji, S., Ye, J.: Canonical Correlation Analysis for Multi-label Classification: A Least-Squares Formulation, Extensions, and Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 194–200 (2011)CrossRefGoogle Scholar
  9. 9.
    Witten, D., Tibshirani, R., Hastie, T.: A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009)CrossRefGoogle Scholar
  10. 10.
    Hardoon, D., Shawe-Taylor, J.: Sparse canonical analysis (2009), http://arxiv.org/abs10908.2724vi
  11. 11.
    Hsieh, W.: Nonlinear canonical correlation analysis by neural network. Neural Networks 13, 1095–1105 (2000)CrossRefGoogle Scholar
  12. 12.
    Matthew, B., Jacquelyn, A., et al.: Semi-supervised Kernel Canonical Correlation Analysis with Application to Human fMRI. Pattern Recognition Letters, 1–16 (2011)Google Scholar
  13. 13.
    Sun, T., Chen, S.: Locality preserving CCA with applications to data visualization and pose estimation. Image and Vision Computing 25, 531–543 (2007)zbMATHCrossRefGoogle Scholar
  14. 14.
    Peng, Y., Zhang, D., Zhang, J.: A New Canonical Correlation Analysis Algorithm with Local Discrimination. Neural Process Letter 31, 1–15 (2010)CrossRefGoogle Scholar
  15. 15.
    Guan, N., Tao, D.,, Luo, Z., Yuan, B.: Manifold Regularized Discriminative Non-negative Matrix Factorization with Fast Gradient Descent. IEEE Transaction on Image Processing 20, 2030–2048 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zhou, T., Tao, D., Wu, X.: Manifold elastic net: a unified framework for sparse dimension reduction. Data Mining and Knowledge Discovering 22, 340–371 (2011)MathSciNetzbMATHCrossRefGoogle Scholar

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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