{ "id": "2308.05814", "version": "v1", "published": "2023-08-10T18:30:11.000Z", "updated": "2023-08-10T18:30:11.000Z", "title": "Randomized low-rank approximations beyond Gaussian random matrices", "authors": [ "Arvind K. Saibaba", "Agnieszka Międlar" ], "comment": "37 pages, appendix is really supplementary materials", "categories": [ "math.NA", "cs.NA" ], "abstract": "This paper expands the analysis of randomized low-rank approximation beyond the Gaussian distribution to four classes of random matrices: (1) independent sub-Gaussian entries, (2) independent sub-Gaussian columns, (3) independent bounded columns, and (4) independent columns with bounded second moment. Using a novel interpretation of the low-rank approximation error involving sample covariance matrices, we provide insight into the requirements of a \\textit{good random matrix} for the purpose of randomized low-rank approximation. Although our bounds involve unspecified absolute constants (a consequence of the underlying non-asymptotic theory of random matrices), they allow for qualitative comparisons across distributions. The analysis offers some details on the minimal number of samples (the number of columns $\\ell$ of the random matrix $\\boldsymbol\\Omega$) and the error in the resulting low-rank approximation. We illustrate our analysis in the context of the randomized subspace iteration method as a representative algorithm for low-rank approximation, however, all the results are broadly applicable to other low-rank approximation techniques. We conclude our discussion with numerical examples using both synthetic and real-world test matrices.", "revisions": [ { "version": "v1", "updated": "2023-08-10T18:30:11.000Z" } ], "analyses": { "keywords": [ "random matrix", "randomized low-rank approximation", "gaussian random matrices", "independent sub-gaussian entries", "independent sub-gaussian columns" ], "note": { "typesetting": "TeX", "pages": 37, "language": "en", "license": "arXiv", "status": "editable" } } }