Sparse tensor recovery via N-mode FISTA with support augmentation
For researchers in sparse tensor recovery, this work offers an incremental improvement over N-mode FISTA by handling support estimation errors.
The paper proposes a four-stage method for sparse tensor recovery that augments the support set to address failures of N-mode FISTA, achieving similar or higher accuracy and often faster speed on synthetic data.
A common approach for performing sparse tensor recovery is to use an N-mode FISTA method. However, this approach may fail in some cases by missing some values in the true support of the tensor and compensating by erroneously assigning nearby values to the support. This work proposes a four-stage method for performing sparse tensor reconstruction that addresses a case where N-mode FISTA may fail by augmenting the support set. Moreover, the proposed method preserves a Tucker-like structure throughout computations for computational efficiency. Numerical results on synthetic data demonstrate that the proposed method produces results with similar or higher accuracy than N-mode FISTA, and is often faster.