Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization

Andersen Man Shun Ang, Jeremy E. Cohen, Nicolas Gillis, Le Thi Khanh Hien

Abstract: This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates the empirical convergence speed of most existing block-coordinate algorithms for dense NTF, in particular for challenging computational scenarios, while requiring a negligible additional computational budget.