Webmodel for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is …
MATRIX ANALYSIS AND APPLICATIONS - Cambridge
Web23 sep. 2024 · Download PDF Abstract: We study the robust recovery of a low-rank matrix from sparsely and grossly corrupted Gaussian measurements, with no prior knowledge on the intrinsic rank. We consider the robust matrix factorization approach. We employ a robust $\ell_1$ loss function and deal with the challenge of the unknown rank by using an … WebYuxin Chen - Wharton Statistics and Data Science insync news
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Web24 sep. 2024 · Nonconvex Robust Low-rank Matrix Recovery. In this paper we study the problem of recovering a low-rank matrix from a number of random linear measurements that are corrupted by outliers taking arbitrary values. We consider a nonsmooth nonconvex formulation of the problem, in which we explicitly enforce the low-rank property of the … WebFast and Robust Fixed-Rank Matrix Recovery German Ros*, Student Member, IEEE, and Julio Guerrero Abstract—We address the problem of efficient sparse fixed-rank (S-FR) matrix decomposition, i.e ... WebOur theoretical and experimental results suggest that the proposed row-and-column affine measurements scheme, together with our recovery algorithm, may provide a powerful framework for affine matrix reconstruction. insync music video