Abstract
Considering limitations of Linear Discriminant Analysis (LDA) and Marginal Fisher Analysis (MFA), a novel discriminant analysis called Local Correlation Discriminant Analysis (LCDA) is proposed in this paper. The main idea behind LCDA is to use more robust similarity measure, correlation metric, to measure the local similarity between image data. This results in better classification performance. In addition, to further improve the discriminant power of LCDA, we extend LCDA to semi-supervised case, which can make use of both labeled and unlabeled data to perform discriminant analysis. Extensive experimental results on ORL and AR face databases demonstrate that the proposed LCDA and its semi-supervised version are superior to Principal Component Analysis (PCA), LDA, CEA, and MFA.
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References
M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1991)1, 71–86.
P. N. Belhumeur, J. P. Hepanha, and D. J. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1997)7, 711–720.
X. He and P. Niyogi. Locality Preserving Projections. In Advances in Neural Information Processing System 16, Cambridge, MA, MIT Press, 2003, 100–115.
S. Yan, D. Xu, B. Zhang, and H. Zhang. Graph embedding and extension: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2007)1, 40–50.
Y. Fu and T. S. Huang. Correlation embedding analysis. In Proceedings of IEEE International Conference on Image Processing, San Diego, CA, 2008, 1696–1699.
V. N. Vapnik. Statistical Learning Theory. New York, John Wiley & Sons, Inc., 1998, 231–259.
O. Chapelle, J. Weston, and B. Schölkopf. Cluster Kernels For Semi-supervised Learning. In Advances in Neural Information Processing Systems, Cambridge, MA, USA, MIT Press, 2003. 585–592.
D. Cai, X. He, and J. Han. Semi-supervised discriminant analysis. In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007, 1–7.
Y. Fu, M. Liu, and T. S. Huang. Conformal embedding analysis with local graph modeling on the unit hypersphere. IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Component Analysis, Minneapolis, Minnesota, USA, 2007, 189–196.
B. V. K. Vijaya Kumar, A. Mahalanobis, and R. D. Juday. Correlation Pattern Recognition. UK, Cambridge University Press, 2006.
T. -K. Kim, J. Kittler, and R. Cippola. Discriminative learning and recognition of image set classes using canonical correlations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2007)6, 1005–1018.
Y. Ma, S. Lao, E. Takikawa, and M. Kawade. Discriminant analysis in correlation similarity measure space. In Proceedings of International Conference on Machine Learning, Oregon, USA, 2007, 577–584.
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Supproted by the National Natural Science Foundation of China (No. 60875004), the Natural Science Foundation of Jiangsu Province of China (No. BK2009184), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 07KJB520133).
Communication author: Chen Caikou, born in 1967, male, Ph.D., Associate Professor.
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Chen, C., Shi, J. Local correlation discriminant analysis and its semi-supervised extension. J. Electron.(China) 28, 289–296 (2011). https://doi.org/10.1007/s11767-011-0535-7
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DOI: https://doi.org/10.1007/s11767-011-0535-7