Tensor completion via a multi-linear low-n-rank factorization model
We extend the low-rank matrix completion problem to a low-n-rank tensor completion problem. An efficient algorithm based on the multi-linear low-n-rank factorization model is proposed in this paper. The nonlinear GaussSeidal method that only requires solving a linear least squares problem per iteration is applied to solve this model. The proposed algorithm is much less computational cost than the trace norm minimization algorithm especially facing the large data.
tensor completion multi-linear low-n-rank factorization nonlinear gaussseidal method singular value decomposition