PROJECT TITLE :
A Rank-One Tensor Updating Algorithm for Tensor Completion
During this letter, we propose a rank-one tensor updating algorithm for solving tensor completion problems. Unlike the existing methods which penalize the tensor by using the total of nuclear norms of unfolding matrices, our optimization model directly employs the tensor nuclear norm which is studied recently. Beneath the framework of the conditional gradient technique, we show that at every iteration, solving the proposed model amounts to computing the tensor spectral norm and the connected rank-one tensor. As a result of the problem of finding the connected rank-one tensor is NP-arduous, we propose a subroutine to unravel it approximately, that is of low computational complexity. Experimental results on real datasets show that our algorithm is economical and effective.
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