FasTer: Fast Tensor Completion with Nonconvex Regularization
This work addresses efficiency and accuracy issues in tensor completion for real-world applications, representing an incremental improvement over existing methods.
The authors tackled the low-rank tensor completion problem by replacing the convex overlapping nuclear norm with a nonconvex regularizer to reduce bias in estimations, resulting in a proposed algorithm that runs orders faster with less memory and achieves much better recovery performance in experiments.
Low-rank tensor completion problem aims to recover a tensor from limited observations, which has many real-world applications. Due to the easy optimization, the convex overlapping nuclear norm has been popularly used for tensor completion. However, it over-penalizes top singular values and lead to biased estimations. In this paper, we propose to use the nonconvex regularizer, which can less penalize large singular values, instead of the convex one for tensor completion. However, as the new regularizer is nonconvex and overlapped with each other, existing algorithms are either too slow or suffer from the huge memory cost. To address these issues, we develop an efficient and scalable algorithm, which is based on the proximal average (PA) algorithm, for real-world problems. Compared with the direct usage of PA algorithm, the proposed algorithm runs orders faster and needs orders less space. We further speed up the proposed algorithm with the acceleration technique, and show the convergence to critical points is still guaranteed. Experimental comparisons of the proposed approach are made with various other tensor completion approaches. Empirical results show that the proposed algorithm is very fast and can produce much better recovery performance.