论文标题
DPAR2：不规则密度张量的快速可扩展的Parafac2分解
DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors
论文作者
论文摘要
给定密度不规则的张量，我们如何有效地分析它？不规则的张量是矩阵的集合，其圆柱的大小相同，行彼此具有不同的尺寸。 PARAFAC2分解是处理在包括表型发现和趋势分析在内的不规则张量的基本工具。尽管存在几种PARAFAC2分解方法，但由于张量涉及昂贵的计算，它们的效率受到不规则致密张量的限制。在本文中，我们提出了DPAR2，这是一种用于不规则致密张量的快速且可扩展的PARAFAC2分解方法。 DPAR2通过有效压缩给定不规则张量的每个切片矩阵，通过压缩结果仔细地重新排序的每个切片矩阵，并利用张量的不规则性来实现高效率。广泛的实验表明，DPAR2的速度比现实世界中不规则张量的竞争对手快6.0倍，同时达到了可比的精度。另外，DPAR2相对于张量的大小和目标等级是可扩展的。
Given an irregular dense tensor, how can we efficiently analyze it? An irregular tensor is a collection of matrices whose columns have the same size and rows have different sizes from each other. PARAFAC2 decomposition is a fundamental tool to deal with an irregular tensor in applications including phenotype discovery and trend analysis. Although several PARAFAC2 decomposition methods exist, their efficiency is limited for irregular dense tensors due to the expensive computations involved with the tensor. In this paper, we propose DPar2, a fast and scalable PARAFAC2 decomposition method for irregular dense tensors. DPar2 achieves high efficiency by effectively compressing each slice matrix of a given irregular tensor, careful reordering of computations with the compression results, and exploiting the irregularity of the tensor. Extensive experiments show that DPar2 is up to 6.0x faster than competitors on real-world irregular tensors while achieving comparable accuracy. In addition, DPar2 is scalable with respect to the tensor size and target rank.
