49 citations · 50 across the 5 of their papers we have counts for
5 papers
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…
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…
Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage
Pei-Chun Su, Stephen Miller, Salim Idriss +2
We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdo…
Optimal Recovery of Precision Matrix for Mahalanobis Distance from High Dimensional Noisy Observations in Manifold Learning
Matan Gavish, Ronen Talmon, Pei-Chun Su +1
Motivated by establishing theoretical foundations for various manifold learning algorithms, we study the problem of Mahalanobis distance (MD), and the associated precision matrix,…
Fetus: the radar of maternal stress, a cohort study
Silvia M. Lobmaier, Alexander Mueller, Camilla Zelgert +10
Objective: We hypothesized that prenatal stress (PS) exerts lasting impact on fetal heart rate (fHR). We sought to validate the presence of such PS signature in fHR by measuring co…