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.
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Zhang, X., Guan, N., Luo, Z., Lan, L. (2012). Discriminative Locality Preserving Canonical Correlation Analysis. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_43
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DOI: https://doi.org/10.1007/978-3-642-33506-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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