Learning canonical correlations of paired tensor sets via tensor-to-vector projection

Authors:
Haiping Lu
Affiliations:
Institute for Infocomm Research Singapore
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Canonical correlation analysis (CCA) is a useful technique for measuring relationship between two sets of vector data. For paired tensor data sets, we propose a multilinear CCA (MCCA) method. Unlike existing multilinear variations of CCA, MCCA extracts uncorrelated features under two architectures while maximizing paired correlations. Through a pair of tensor-to-vector projections, one architecture enforces zero-correlation within each set while the other enforces zero-correlation between different pairs of the two sets. We take a successive and iterative approach to solve the problem. Experiments on matching faces of different poses show that MCCA outperforms CCA and 2D- CCA, while using much fewer features. In addition, the fusion of two architectures leads to performance improvement, indicating complementary information.