Learning Canonical Correlations of Paired Tensor Sets Via Tensor-to-Vector Projection
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1516-1521.
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.
@inproceedings{ijcai2013p226,
title = {Learning Canonical Correlations of Paired Tensor Sets Via Tensor-to-Vector Projection},
author = {Lu, Haiping},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1516--1521},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/226},
}
TY - CPAPER TI - Learning Canonical Correlations of Paired Tensor Sets Via Tensor-to-Vector Projection AU - Lu, Haiping T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1516 EP - 1521 UR - https://ijcai.org/proceedings/2013/226 ER -Copied
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