Abstract
Tensor completion aims to recover an unknown low-rank or approximately low-rank tensor from a sampling set of its entries. It is shown that this problem can be solved via iterative hard thresholding method. However the orthogonality of the current step and the previous step leads to its slow convergence speed. In this paper, we propose an accelerated iterative hard thresholding method for tensor completion (ATIHT) by combining the current gradient and directions of several previous iterative steps as new search direction. We make some comparison between numerical tests of our algorithm and TIHT, FP-LRTC and HaLRTC algorithms on randomly generated tensors and low-rank color image recovery problem. The results suggest that our method is more effective and promising.
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