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Sufficient Canonical Correlation Analysis

Guo, Y; Ding, X; Liu, C; Xue, J-H; (2016) Sufficient Canonical Correlation Analysis. IEEE Transactions on Image Processing , 25 (6) pp. 2610-2619. 10.1109/TIP.2016.2551374. Green open access

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Canonical correlation analysis (CCA) is an effective way to find two appropriate subspaces in which Pearson’s correlation coefficients are maximized between projected random vectors. Due to its well-established theoretical support and relatively efficient computation, CCA is widely used as a joint dimension reduction tool and has been successfully applied to many image processing and computer vision tasks. However, as reported, the traditional CCA suffers from overfitting in many practical cases. In this paper, we propose sufficient CCA (S-CCA) to relieve CCA’s overfitting problem, which is inspired by the theory of sufficient dimension reduction. The effectiveness of S-CCA is verified both theoretically and experimentally. Experimental results also demonstrate that our S-CCA outperforms some of CCA’s popular extensions during the prediction phase, especially when severe overfitting occurs.

Type: Article
Title: Sufficient Canonical Correlation Analysis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIP.2016.2551374
Publisher version: http://dx.doi.org/10.1109/TIP.2016.2551374
Language: English
Additional information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Canonical correlation analysis, multi-class classification, multi-view learning, generalization ability, overfitting, sufficient dimension reduction.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1478134
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