Within the tensor singular value decomposition (T-SVD) framework, existing robust low-ranktensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these met...
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Within the tensor singular value decomposition (T-SVD) framework, existing robust low-ranktensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rankapproximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-ranktensorapproximation approaches fusing random projection techniques are first devised under the order-d ( d >= 3 ) T-SVD framework. Theoretical results on error bounds for the proposed randomized algorithms are provided. On this basis, we then further investigate the robust high-order tensor completion problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. Experimental results on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.
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