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.
Original language | English |
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Pages | 2949-2955 |
Number of pages | 7 |
Publication status | Published - 2017 |
Event | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: 4 Feb 2017 → 10 Feb 2017 |
Conference
Conference | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
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Country/Territory | United States |
City | San Francisco |
Period | 4/02/17 → 10/02/17 |
Scopus Subject Areas
- Artificial Intelligence