AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations
Yang Zhou, Haiping Lu, Yiu-ming Cheung

Last modified: 2017-02-13

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.

Keywords


Dimensionality Reduction; Probabilistic Model; Bilinear CCA

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