Towards Practical Alternating Least-Squares for CCA

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Zhiqiang Xu, Ping Li


Alternating least-squares (ALS) is a simple yet effective solver for canonical correlation analysis (CCA). In terms of ease of use, ALS is arguably practitioners' first choice. Despite recent provably guaranteed variants, the empirical performance often remains unsatisfactory. To promote the practical use of ALS for CCA, we propose truly alternating least-squares. Instead of approximately solving two independent linear systems, in each iteration, it simply solves two coupled linear systems of half the size. It turns out that this coupling procedure is able to bring significant performance improvements in practice. Inspired by accelerated power method, we further propose faster alternating least-squares, where momentum terms are introduced into the update equations. Both algorithms enjoy linear convergence. To make faster ALS even more practical, we put forward adaptive alternating least-squares to avoid tuning the momentum parameter, which is as easy to use as the plain ALS while retaining advantages of the fast version. Experiments on several datasets empirically demonstrate the superiority of the proposed algorithms to recent variants.