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Tensor completion and Tensor recovery from Gaussian measurements
Introduction
In our work [1], we give the exact recovery guarantees of tensor completion and tensor recovery from Gaussian measurements by tensor nuclear norm minimization. The tensor nuclear norm was proposed in our works [3][4]. A more general tensor nuclear norm undear general invertible linear transform was proposed in [5] and applied to tensor completion [5] and tensor robust PCA [6].
We provide the codes of the following two models in [1].
Tensor completion by tensor nuclear norm minimization
Tensor recovery from Gaussian measurements by tensor nuclear norm minimization
Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan. Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm. TPAMI. 2019
Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan. Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization. CVPR, 2016
Canyi Lu, Xi Peng, Yunchao Wei. Low-Rank Tensor Completion With a New Tensor Nuclear Norm Induced by Invertible Linear Transforms. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Canyi Lu, Pan Zhou. Exact Recovery of Tensor Robust Principal Component Analysis under Linear Transforms. arXiv preprint arXiv:1907.08288. 2019
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tensor completion/tensor recovery from Gaussian measurements by tensor nuclear norm minimization