High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization

TL;DR

This paper introduces a novel tensor completion algorithm called Sparse Tensor-train Optimization (STTO) that effectively recovers high-dimensional data, especially under very high missing rates, outperforming existing methods.

Contribution

The paper proposes a new tensor completion method using tensor-train decomposition and sparse optimization, with tensorization to improve performance on high-order data.

Findings

Outperforms existing algorithms in image recovery tasks.

Maintains high accuracy even with 90-99% missing data.

Effective for both low-order and high-order tensor data.

Abstract

In this paper, we aim at the problem of tensor data completion. Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tensor order. We propose an algorithm named Sparse Tensor-train Optimization (STTO) which considers incomplete data as sparse tensor and uses first-order optimization method to find the factors of tensor-train decomposition. Our algorithm is shown to perform well in simulation experiments at both low-order cases and high-order cases. We also employ a tensorization method to transform data to a higher-order form to enhance the performance of our algorithm. The results of image recovery experiments in various cases manifest that our method outperforms other completion algorithms. Especially when the missing rate is very high, e.g., 90\% to 99\%, our method is significantly better than the state-of-the-art methods.