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Most recent low-rank tensor completion algorithms are based on tensor nuclear norm minimization problems. The convex relaxation problem of tensor n-rank minimization has to be solved iteratively and involves multiple singular value decompositions (SVDs) at each iteration, and thus such algorithms suffer from high computation cost. In this letter, we propose an efficient low-rank tensor completion approach. First, we introduce a matrix factorization idea into the tensor nuclear norm model, and then can achieve a much smaller scale matrix nuclear norm minimization problem. Moreover, we develop an efficient iterative scheme for solving the proposed model with orthogonality constraint. Our extensive evaluation results validate both the effectiveness and efficiency of the proposed approach.