70 research outputs found
Convolutional Graph-Tensor Net for Graph Data Completion
Graph data completion is a fundamentally important issue as data generally
has a graph structure, e.g., social networks, recommendation systems, and the
Internet of Things. We consider a graph where each node has a data matrix,
represented as a \textit{graph-tensor} by stacking the data matrices in the
third dimension. In this paper, we propose a \textit{Convolutional Graph-Tensor
Net} (\textit{Conv GT-Net}) for the graph data completion problem, which uses
deep neural networks to learn the general transform of graph-tensors. The
experimental results on the ego-Facebook data sets show that the proposed
\textit{Conv GT-Net} achieves significant improvements on both completion
accuracy (50\% higher) and completion speed (3.6x 8.1x faster) over the
existing algorithms
Non-convex approaches for low-rank tensor completion under tubal sampling
Tensor completion is an important problem in modern data analysis. In this
work, we investigate a specific sampling strategy, referred to as tubal
sampling. We propose two novel non-convex tensor completion frameworks that are
easy to implement, named tensor - (TL12) and tensor completion via
CUR (TCCUR). We test the efficiency of both methods on synthetic data and a
color image inpainting problem. Empirical results reveal a trade-off between
the accuracy and time efficiency of these two methods in a low sampling ratio.
Each of them outperforms some classical completion methods in at least one
aspect
- …