Nonconvex Low Tubal Rank Tensor Minimization
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2956115
Abstract: In the sparse vector recovery problem, the $L_{0}$ -norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can… read more here.
Keywords: function; tensor; rank; rank tensor ... See more keywords
Low-Tubal-Rank Plus Sparse Tensor Recovery With Prior Subspace Information
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2020.2986773
Abstract: Tensor principal component pursuit (TPCP) is a powerful approach in the tensor robust principal component analysis (TRPCA), where the goal is to decompose a data tensor to a low-tubal-rank part plus a sparse residual. TPCP… read more here.
Keywords: tubal rank; plus sparse; prior subspace; tensor ... See more keywords