Weize Sun1, Peng Zhang1,*, Jingxin Xu2, Huochao Tan3
CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2111-2126, 2021, DOI:10.32604/cmc.2021.016804
Abstract This paper addresses the problem of tensor completion from limited samplings. Generally speaking, in order to achieve good recovery result, many tensor completion methods employ alternative optimization or minimization with SVD operations, leading to a high computational complexity. In this paper, we aim to propose algorithms with high recovery accuracy and moderate computational complexity. It is shown that the data to be recovered contains structure of Kronecker Tensor decomposition under multiple patterns, and therefore the tensor completion problem becomes a Kronecker rank optimization one, which can be further relaxed into tensor Frobenius-norm minimization with a constraint of a maximum number… More >
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