论文标题
非负张量分解的加速块坐标下降
Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization
论文作者
论文摘要
本文涉及改善近似非负张量分解（NTF）的块坐标下降算法的经验收敛速度。我们提出了块更新之间的推断策略，称为启发式外推（她）（她）。她显着加速了密集NTF的大多数现有区块坐标算法的经验收敛速度，尤其是针对挑战的计算方案，同时需要可忽略的额外计算预算。
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.
