2 papers
math.SP2026
Data-Driven Matrix Recovery via Optimal Shrinkage and Spatially Resolved Singular Vector Denoising under High-Dimensional Separable Noise
Pei-Chun Su
This paper develops a spatially resolved perturbation theory for singular vectors under high-dimensional separable noise and applies it to data-driven matrix recovery. In the asymp…
stat.AP2024
Data-Driven optimal shrinkage of singular values under high-dimensional noise with separable covariance structure with application
Pei-Chun Su, Hau-Tieng Wu
We develop a data-driven optimal shrinkage algorithm for matrix denoising in the presence of high-dimensional noise with a separable covariance structure; that is, the noise is col…