Global and Local Structure Learning for Sparse Tensor Completion
This addresses the limitation in tensor decomposition methods for accurate completion in applications like data analysis, though it appears incremental as it builds on existing tensor and GNN techniques.
The paper tackles the problem of tensor completion by proposing TGL, a method that learns global and local structures without prior knowledge, achieving improved accuracy in predicting missing entries.
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.