17 citations · 40 across the 7 of their papers we have counts for
7 papers
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion
Morteza Ashraphijuo, Vaneet Aggarwal, Xiaodong Wang
In this letter, we study the deterministic sampling patterns for the completion of low rank matrix, when corrupted with a sparse noise, also known as robust matrix completion. We e…
Scaled Nuclear Norm Minimization for Low-Rank Tensor Completion
Morteza Ashraphijuo, Xiaodong Wang
Minimizing the nuclear norm of a matrix has been shown to be very efficient in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of nuclear norms of matrici…
Rank Determination for Low-Rank Data Completion
Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal
Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper,…
Fundamental Conditions for Low-CP-Rank Tensor Completion
Morteza Ashraphijuo, Xiaodong Wang
We consider the problem of low canonical polyadic (CP) rank tensor completion. A completion is a tensor whose entries agree with the observed entries and its rank matches the given…
Characterization of Deterministic and Probabilistic Sampling Patterns for Finite Completability of Low Tensor-Train Rank Tensor
Morteza Ashraphijuo, Xiaodong Wang
In this paper, we analyze the fundamental conditions for low-rank tensor completion given the separation or tensor-train (TT) rank, i.e., ranks of unfoldings. We exploit the algebr…
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data
Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal
We consider the multi-view data completion problem, i.e., to complete a matrix where the ranks of , and $\mathbf{U…