• OpenAccess
  • Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition  [AIDM 2015]
  • DOI: 10.4236/jcc.2015.311011   PP.64 - 70
  • Author(s)
  • Jing Wang, Fang Chen, Quanxue Gao
  • Dimensionality reduction is very important in pattern recognition, machine learning, and image recognition. In this paper, we propose a novel linear dimensionality reduction technique using trace ratio criterion, namely Discriminant Neighbourhood Structure Embedding Using Trace Ratio Criterion (TR-DNSE). TR-DNSE preserves the local intrinsic geometric structure, characterizing properties of similarity and diversity within each class, and enforces the separability between different classes by maximizing the sum of the weighted distances between nearby points from different classes. Experiments on four image databases show the effectiveness of the proposed approach.

  • Dimensionality Reduction, Manifold Learning, Variability, Trace Ratio
  • References
  • [1]
    Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. 2nd Edition, Academic Press.
    Jolliffe, T. (1986) Principal Component Analysis. Springer-Verlag, New York.
    Strassen, V. (1969) Gaussian Elimination Is Not Optimal. Numer Math., 13, 54-356.
    Tao, D., Li, X., Wu, X. and Maybank, S.J. (2007) General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. IEEE Trans. Pattern Anal. Mach. Intell., 29, 1700-1714.
    Saul, L.K. and Roweis, S.T. (2003) Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. J. Mach. Learn. Res., 4, 119-155.
    Roweis, S. and Saul, L. (2000) Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290, 2323-2326.
    Belkin, M. and Niyogi, P. (2003) Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 15, 1373-1396.
    He, X., Cai, D., Yan, S. and Zhang, H. (2005) Neighbourhood Preserving Embedding. Proc. ICCV.
    Lai, Z., Wan, M., Jin, Z. and Yang, J. (2011) Sparse Two-Dimensional Local Discriminant Projections for Feature Extraction. Neurocomputing, 74, 629-637.
    Yu, J., Liu, D., Tao, D. and Seah, H.S. (2011) Complex Object Corresponding Construction in Two-Dimensional Animation. IEEE Trans. Image Processing, 20, 3257-3269.
    Xu, Y., Feng, G. and Zhao, Y. (2009) One Improvement to Two-Dimensional Locality Preserving Projection Method for Use with Face Recognition. Neurocomputing, 73, 245-249.
    Xu, D., Yan, S., Tao, D., et al. (2007) Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content-Based Image Retrieval. IEEE Transactions on Image Processing, 16, 2811-2821.
    Gao, Q., Xu, H., Li, Y. and Xie, D. (2010) Two-Dimensional Supervised Local Similarity and Diversity Projection, Pattern Recognition, 43, 3359-3363.
    Zhang, T., Tao, D., Li, X. and Yang, J. (2009) Patch Alignment for Dimensionality Reduction. IEEE Trans. Knowl. Data Eng., 21, 1299-1313.
    Li, B., Zheng, C.H. and Huang, D.S. (2008) Locally Linear Discriminant Embedding: An Efficient Method for Face Recognition. Pattern Recognition, 41, 3813-3821.
    Weinberger, K.Q. and Saul, L.K. (2004) Unsupervised Learning of Image Manifolds by Semi-Definite Programming. Proc. IEEE Con. Computer Vision and Pattern Recognition’2004, 988-995.
    Weinberger, K.Q., Packer, B.D. and Saul, L.K. (2005) Nonlinear Dimensionality Reduction by Semi-Definite Programming and Kernel Matrix Factorization. Proc. the Tenth Workshop Artificial Intelligence and Statistics (AISTATS- 2005), 381-388.
    Zhou, T., Tao, D. and Wu, X. (2011) Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction. Data Min. Knowl. Disc., 22, 340-371.
    Gao, Q., Zhang, H. and Liu, J. (2012) Two-Dimensional Margin, Similarity and Variation Embedding. Neurocomputing, 86, 179-183.
    Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q. and Lin, S. (2007) Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. IEEE Trans. Pattern Anal. Mach. Intell., 29, 40-51.
    Nie, F., Xiang, S. and Zhang, C. (2007) Neighbourhood Minmax Projections. IJCAI-07, 993-998.
    Belhumeur, P., Hespanha, P. and Kriegman, D. (1997) Eigenfaces vs. fisherfaces: Recognition Using Class Specific Linear Projection. IEE Trans. Pattern Anal. Mach. Intell., 19, 711-720.
    Gao, Q., Liu, J., Zhang, H., Hou, J. and Yang, X. (2012) Enhanced Fisher Discriminant Criterion for Image Recognition. Pattern Recognition, 45, 3717-3724.
    Phillips, P., Moon, H., Rizvi, S. and Rauss, P. (2000) The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 22, 1090-1104.
    Nene, S.A., Nayar, S.K. and Murase, H. (1996) Columbia Object Image Library (COIL-20). Technical Report CUCS- 005-96.

Engineering Information Institute is the member of/source content provider to