  The sum-of-correlations (SUMCOR) formulation of generalized canonical
correlation analysis (GCCA) seeks highly correlated low-dimensional
representations of different views via maximizing pairwise latent similarity of
the views. SUMCOR is considered arguably the most natural extension of
classical two-view CCA to the multiview case, and thus has numerous
applications in signal processing and data analytics. Recent work has proposed
effective algorithms for handling the SUMCOR problem at very large scale.
However, the existing scalable algorithms cannot incorporate structural
regularization and prior information -- which are critical for good performance
in real-world applications. In this work, we propose a new computational
framework for large-scale SUMCOR GCCA that can easily incorporate a suite of
structural regularizers which are frequently used in data analytics. The
updates of the proposed algorithm are lightweight and the memory complexity is
also low. In addition, the proposed algorithm can be readily implemented in a
parallel fashion. We show that the proposed algorithm converges to a
Karush-Kuhn-Tucker (KKT) point of the regularized SUMCOR problem. Judiciously
designed simulations and real-data experiments are employed to demonstrate the
effectiveness of the proposed algorithm.
