The Canonical Polyadic (CP) tensor decomposition is frequently used as a model in applications in a variety of different fields. Using jackknife resampling to estimate parameter uncertainties is often desirable but results in an increase of the already high computational cost. Upon observation that the resampled tensors, though different, are nearly identical, we show that it is possible to extend the recently proposed Concurrent ALS (CALS) technique to a jackknife resampling scenario. This extension gives access to the computational efficiency advantage of CALS for the price of a modest increase (typically a few percent) in the number of floating point operations. Numerical experiments on both synthetic and real-world datasets demonstrate that the new workflow based on a CALS extension can be several times faster than a straightforward workflow where the jackknife submodels are processed individually.
翻译:Canonical Policadic (CP) 高压分解常被用作不同领域应用的模型。 使用 joknife 重新取样来估计参数的不确定性往往是可取的, 但导致本已高昂的计算成本增加。 在发现重新采样的电压虽然不同,但几乎相同时,我们表明,可以将最近提出的并行ALS (CALS) 技术推广到切克尼fe (CALS) 重新采样的假想中。 这一扩展使得CALS 的计算效率优势能够达到浮点操作数量略微增加( 通常只有几个百分点) 的价格。 合成和真实世界数据集的数值实验都表明,基于 CALS 扩展的新工作流程可能比单独处理 jknife 子模型的直径工作流程快几倍。