Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence
Sign Up to like & getrecommendations! Published in 2019 at "Foundations of Computational Mathematics"
DOI: 10.1007/s10208-020-09490-9
Abstract: The task of recovering a low-rank matrix from its noisy linear measurements plays a central role in computational science. Smooth formulations of the problem often exhibit an undesirable phenomenon: the condition number, classically defined, scales… read more here.
Keywords: matrix; optimization; rank matrix; low rank ... See more keywords
Hyperspectral Image Denoising Using Adaptive Weight Graph Total Variation Regularization and Low-Rank Matrix Recovery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3113078
Abstract: Hyperspectral image (HSI) is often corrupted by various kinds of noises. This letter proposes an innovative HSI denoising approach by leveraging the graph signal processing (GSP) theory and the low-rank (LR) matrix recovery model. With… read more here.
Keywords: rank matrix; hyperspectral image; hsi; low rank ... See more keywords