Robust Matrix Completion with Deterministic Sampling via Convex Optimization
<|reference_start|>Robust Matrix Completion with Deterministic Sampling via Convex Optimization: This paper deals with the problem of robust matrix completion -- retrieving a low-rank matrix and a sparse matrix from the compressed counterpart of their superposition. Though seemingly not an unresolved issue, we point out that the compressed matrix in our case is sampled in a deterministic pattern instead of those random ones on which existing studies depend. In fact, deterministic sampling is much more hardware-friendly than random ones. The limited resources on many platforms leave deterministic sampling the only choice to sense a matrix, resulting in the significance of investigating robust matrix completion with deterministic pattern. In such spirit, this paper proposes \textit{restricted approximate $\infty$-isometry property} and proves that, if a \textit{low-rank} and \textit{incoherent} square matrix and certain deterministic sampling pattern satisfy such property and two existing conditions called \textit{isomerism} and \textit{relative well-conditionedness}, the exact recovery from its sampled counterpart grossly corrupted by a small fraction of outliers via convex optimization happens with very high probability.<|reference_end|>
@article{wang2024robust,
    title={Robust Matrix Completion with Deterministic Sampling via Convex
  Optimization},
    author={Yinjian Wang},
    journal={arXiv preprint arXiv:2410.05412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05412},
    primaryClass={cs.IT math.IT}
}