  The recent low-rank prior based models solve the tensor completion problem
efficiently. However, these models fail to exploit the local patterns of
tensors, which compromises the performance of tensor completion. In this paper,
we propose a novel hierarchical prior regularized matrix factorization model
for tensor completion. This model hierarchically incorporates the low-rank
prior, total variation prior, and sparse coding prior into a matrix
factorization, simultaneously characterizing both the global low-rank property
and the local smoothness of tensors. For solving the proposed model, we use the
alternating direction method of multipliers to establish our algorithm.
Besides, the complexity and convergence are investigated to further validate
the algorithm effectiveness. The proposed scheme is then evaluated through
various data sets. Experiment results verify that, the proposed method
outperforms several state-of-the-art approaches.
