Block Row Kronecker-Structured Linear Systems With a Low-Rank Tensor Solution

Several problems in compressed sensing and randomized tensor decomposition can be formulated as a structured linear system with constrained the solution. In particular, we consider block row Kronecker-structured systems low multilinear rank singular value decomposition, low-rank canonical polyadic or train this paper, provide algorithms that serve tools for finding such solutions large, higher-order data tensor, given combinations of its entries. Consistent literature on sensing, number entries needed to find solution is far smaller than corresponding total original tensor. We derive conditions under which retrieved from type also generic conditions. Finally, validate our by comparing them related reconstructing hyperspectral image measurements.