Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
<|reference_start|>Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization: Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent method for spectral compressed sensing via symmetric factorization named Symmetric Hankel Projected Gradient Descent (SHGD), which updates only one matrix and avoids a balancing regularization term. SHGD reduces about half of the computation and storage costs compared to the prior gradient method based on asymmetric factorization. {Besides, the symmetric factorization employed in our work is completely novel to the prior low-rank factorization model, introducing a new factorization ambiguity under complex orthogonal transformation}. Novel distance metrics are designed for our factorization method and a linear convergence guarantee to the desired signal is established with $O(r^2\log(n))$ observations. Numerical simulations demonstrate the superior performance of the proposed SHGD method in phase transitions and computation efficiency compared to state-of-the-art methods.<|reference_end|>
@article{li2024projected,
    title={Projected Gradient Descent for Spectral Compressed Sensing via Symmetric
  Hankel Factorization},
    author={Jinsheng Li, Wei Cui, and Xu Zhang},
    journal={arXiv preprint arXiv:2403.09031},
    year={2024},
    doi={10.1109/TSP.2024.3378004},
    archivePrefix={arXiv},
    eprint={2403.09031},
    primaryClass={cs.IR}
}