Abstract
In this paper, a kernel based sparse locality preserving canonical correlation analysis (KSLPCCA) method is presented for high dimensional feature extraction. Unlike many existing techniques such as DCCA and 2D CCA, SLPCCA aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a regularization-related objective function. The obtained projections contain natural discriminating information even if no class labels are provided. As SLPCCA is a linear method, nonlinear extension is further proposed which can map the input space to a high-dimensional feature space. Experimental results demonstrate the efficiency of the proposed method.
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References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
Cooke, T.: Two Variations on Fisher’s Linear Discriminant for Pattern Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 268–273 (2002)
Hardoon, D.R., Szedmak, S., John, S.T.: Canonical correlation analysis: An overview with application to learning methods. Neural Computation 16, 2639–2664 (2004)
Wu, X., Jia, Y., Liang, W.: Incremental discriminant analysis of canonical correlations for action recognition. Pattern Recognition 43, 4190–4197 (2010)
Sun, T., Chen, S., Yang, J., Shi, P.: A Novel Method of Combined Feature Extraction for Recognition. In: Eighth IEEE International Conference on Data Mining, pp. 1043–1048. IEEE Press, New York (2008)
Sun, T., Chen, S.: Locality preserving CCA with applications to data visualization and pose estimation. Image and Vision Computing 25, 531–543 (2007)
Cheng, B., Yang, J., Yan, S., Fu, Y., Huang, T.S.: Learning With ℓ1-Graph for Image Analysis. IEEE Trans. on Image Processing 19(4), 858–866 (2010)
Yang, M., Zhang, L., Yang, J., Zhang, D.: Robust Sparse Coding for Face Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 625–632. IEEE Press, New York (2011)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Intl. Conf. on Neural Information Processing Systems, pp. 801–808. MIT Press (2006)
Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 725–737 (2006)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET Database and Evaluation Procedure for Face-Recognition Algorithms. Image and Vision Computing 16, 295–306 (1998)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE Trans. on Neural Networks 14(1), 117–126 (2003)
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Hu, H. (2012). Kernel Sparse Locality Preserving Canonical Correlation Analysis for Multi-modal Feature Extraction. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_41
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DOI: https://doi.org/10.1007/978-3-642-35136-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35135-8
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