论文标题
随机交替的最小二乘法分析随机张量的分解
Analysis of the Stochastic Alternating Least Squares Method for the Decomposition of Random Tensors
论文作者
论文摘要
随机交替的最小二乘（SALS）是一种近似采样随机张量的平均分解的方法。它的简单性和有效的内存使用使SALSS成为在线环境中分解张量的理想工具。我们在轻度的正则化和有关数据界面的可验证假设下表明，SALS算法是全球收敛的。数值实验验证了我们的理论发现，并证明了该算法的性能和复杂性。
Stochastic Alternating Least Squares (SALS) is a method that approximates the canonical decomposition of averages of sampled random tensors. Its simplicity and efficient memory usage make SALS an ideal tool for decomposing tensors in an online setting. We show, under mild regularization and readily verifiable assumptions on the boundedness of the data, that the SALS algorithm is globally convergent. Numerical experiments validate our theoretical findings and demonstrate the algorithm's performance and complexity.
