Zhou, Y., Lu, H. orcid.org/0000-0002-0349-2181 and Cheung, Y.M. (2017) Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017. The 31st AAAI Conference on Artificial Intelligence (AAAI), 04-09 Feb 2017, San Francisco. Association for the Advancement of Artificial Intelligence , pp. 2949-2955.
Abstract
Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matrix structures in PCCA. However, existing bilinear PCCAs impose restrictive model assumptions for matrix structure preservation, sacrificing generative correctness or model flexibility. To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model. Our new model preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations. This enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation. Experimental results on two real-world applications demonstrate the superior performance of BPCCA over competing methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017, Association for the Advancement of Artificial Intelligence. This is an author produced version of a paper subsequently published in 31st AAAI Conference on Artificial Intelligence, AAAI 2017. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Jan 2017 13:14 |
Last Modified: | 14 Nov 2017 09:34 |
Published Version: | https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/v... |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109950 |