Graph regularized multiset canonical correlations with applications to joint feature extraction |
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Authors: | Yun-Hao Yuan Quan-Sen Sun |
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Affiliation: | 1. Department of Computer Science and Technology, Jiangnan University, No. 1800, Lihu Street, Wuxi 214122, China;2. School of Computer Science and Engineering, Nanjing University of Science & Technology, No. 200, Xiaolingwei Street, Nanjing 210094, Jiangsu, China |
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Abstract: | Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms. |
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Keywords: | Pattern recognition Canonical correlation analysis Multiset canonical correlations Graph embedding Feature extraction |
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