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Graph regularized multiset canonical correlations with applications to joint feature extraction
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,...
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Published in: | Pattern recognition 2014-12, Vol.47 (12), p.3907-3919 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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.
•We propose a novel algorithm called GrMCC for joint feature extraction.•GrMCC considers both discriminative and intrinsic geometrical structure in multi-representation data.•The extracted features by GrMCC have strong discriminant power for recognition.•Experimental results show GrMCC can provide encouraging recognition results in contrast to the state-of-the-art algorithms. |
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ISSN: | 0031-3203 1873-5142 |
DOI: | 10.1016/j.patcog.2014.06.016 |