A Novel Sparsity Measure for Tensor Recovery

Qian Zhao, Deyu Meng, Xu Kong, Qi Xie, Wenfei Cao, Yao Wang, Zongben Xu; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 271-279

Abstract

In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor. The proposed sparsity measure has a natural physical meaning which is intrinsically the size of the fundamental Kronecker basis to express the tensor. By embedding the sparsity measure into the tensor completion and tensor robust PCA frameworks, we formulate new models to enhance their capability in tensor recovery. Through introducing relaxation forms of the proposed sparsity measure, we also adopt the alternating direction method of multipliers (ADMM) for solving the proposed models. Experiments implemented on synthetic and multispectral image data sets substantiate the effectiveness of the proposed methods.

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[bibtex]
@InProceedings{Zhao_2015_ICCV,
author = {Zhao, Qian and Meng, Deyu and Kong, Xu and Xie, Qi and Cao, Wenfei and Wang, Yao and Xu, Zongben},
title = {A Novel Sparsity Measure for Tensor Recovery},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}