通过图神经网络学习张量的局部结构，实现无先验关系的高精度补全
Global and Local Structure Learning for Sparse Tensor Completion
- 用图神经网络捕捉张量各维度间的局部依赖关系
- 在多个数据集上优于现有方法，补全误差显著降低
- 无需预先设定维度间关系，适合关系未知场景
张量补全是预测不完整张量中缺失条目的问题。传统张量分解方法未能有效学习各模式下维度之间的关系，限制了补全精度。且多数模型需事先了解维度间的关系，获取成本高。本文提出TGL（Tensor Decomposition Learning Global and Local Structures），通过图神经网络在无先验知识的情况下学习因子矩阵中的局部结构，实现更准确的张量重建。在多个基准数据集上的大量实验表明，TGL显著优于现有方法，在多种稀疏率下均表现出更强的补全能力。
原文摘要 · Abstract (English)
How can we accurately complete tensors by learning relationships of dimensions along each mode? Tensor completion, a widely studied problem, is to predict missing entries in incomplete tensors. Tensor decomposition methods, fundamental tensor analysis tools, have been actively developed to solve tensor completion tasks. However, standard tensor decomposition models have not been designed to learn relationships of dimensions along each mode, which limits to accurate tensor completion. Also, previously developed tensor decomposition models have required prior knowledge between relations within dimensions to model the relations, expensive to obtain. This paper proposes TGL (Tensor Decomposition Learning Global and Local Structures) to accurately predict missing entries in tensors. TGL reconstructs a tensor with factor matrices which learn local structures with GNN without prior knowledges. Extensive experiments are conducted to evaluate TGL with baselines and datasets.
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