On the Faster Alternating Least-Squares for CCA
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1621-1629, 2021.
We study alternating least-squares (ALS) for canonical correlation analysis (CCA). Recent research shows that the alternating least-squares solver for k-CCA can be directly accelerated with momentum and prominent performance gain has been observed in practice for the resulting simple algorithm. However, despite the simplicity, it is difficult for the accelerated rate to be analyzed in theory in order to explain and match the empirical performance gain. By looking into two neighboring iterations, in this work, we propose an even simpler variant of the faster alternating least-squares solver. Instead of applying momentum to each update for acceleration, the proposed variant only leverages momentum at every other iteration and can converge at a provably faster linear rate of nearly square-root dependence on the singular value gap of the whitened cross-covariance matrix. In addition to the high consistency between theory and practice, experimental studies also show that our variant of the alternating least-squares algorithm as a block CCA solver is even more pass efficient than other variants.