'Association for the Advancement of Artificial Intelligence (AAAI)'
Doi
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