Weize Sun¹, Peng Zhang^1,*, 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 >

Ming Zhao^*, Tao Liu

Journal of Information Hiding and Privacy Protection, Vol.2, No.3, pp. 125-134, 2020, DOI:10.32604/jihpp.2020.010211

Abstract The problem of low accuracy of POI (Points of Interest) recommendation in LBSN (Location-Based Social Networks) has not been effectively solved. In this paper, a POI recommendation algorithm based on nonconvex regularized tensor completion is proposed. The fourth-order tensor is constructed by using the current location category, the next location category, time and season, the regularizer is added to the objective function of tensor completion to prevent over-fitting and reduce the error of the model. The proximal algorithm is used to solve the objective function, and the adaptive momentum is introduced to improve the efficiency of the solution. The experimental… More >