Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization
This work addresses a computational bottleneck for researchers and practitioners in machine learning and data analysis, but it is incremental as it builds on existing block-coordinate descent methods.
The paper tackled the slow convergence of block-coordinate descent algorithms for nonnegative tensor factorization by proposing an extrapolation strategy called HER, which significantly accelerates empirical convergence speed with negligible extra computational cost.
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.