2023/03/07 by Erwin Riegler, Günther Koliander, Riegler, Erwin +5
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #Mathematical Analysis and Transform Methods #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.2303.03731
openalex publication_date 2023/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a theory for matrix completion that goes beyond the low-rank structure commonly considered in the literature and applies to general matrices of low description complexity. Specifically, complexity of the sets of matrices encompassed by the theory is measured in terms of Hausdorff and upper Minkowski dimensions. Our goal is the characterization of the number of linear measurements, with an emphasis on rank-1 measurements, needed for the existence of an algorithm that yields reconstruction, either perfect, with probability 1, or with arbitrarily small probability of error, depending on the setup. Concretely, we show that matrices taken from a set U such that U-U has Hausdorff dimension s can be recovered from k>s measurements, and random matrices supported on a set U of Hausdorff dimension s can be recovered with probability 1 from k>s measurements. What is more, we establish the existence of recovery mappings that are robust against additive perturbations or noise in the measurements. Concretely, we show that there are β-Hölder continuous mappings recovering matrices taken from a set of upper Minkowski dimension s from k>2s/(1-β) measurements and, with arbitrarily small probability of error, random matrices supported on a set of upper Minkowski dimension s from k>s/(1-β) measurements. The numerous concrete examples we consider include low-rank matrices, sparse matrices, QR decompositions with sparse R-components, and matrices of fractal nature.