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 NTF, in particular for challenging computational scenarios, while requiring a negligible additional computational budget.

中文翻译：

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加速非负张量分解的块坐标下降

本文关注的是提高近似非负张量分解 (NTF) 的块坐标下降算法的经验收敛速度。我们提出了一种在块更新之间的外推策略，称为带重新启动的启发式外推（HER）。HER 显着加快了大多数现有的 NTF 块坐标算法的经验收敛速度，特别是对于具有挑战性的计算场景，同时需要的额外计算预算可以忽略不计。