High-Order Tensor Completion for Data Recovery via Sparse Tensor-Train Optimization

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...

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Bibliographic Details
Main Authors: Yuan, Longhao, Zhao, Qibin, Cao, Jianting
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Data models
incomplete data
Matrix decomposition
Optimization
Signal processing algorithms
Tensile stress
tensor completion
tensor-train decomposition
tensorization
Visualization
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Summary: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 ten-sorization 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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462592