Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- January 2020
- DOI:
- 10.48550/arXiv.2001.04321
- arXiv:
- arXiv:2001.04321
- Bibcode:
- 2020arXiv200104321A
- Keywords:
-
- Mathematics - Numerical Analysis;
- Computer Science - Machine Learning;
- Mathematics - Optimization and Control;
- Statistics - Machine Learning
- E-Print:
- 32 pages, 24 figures