1 citations · 1 across the 3 of their papers we have counts for
5 papers
The Sharp Phase Transition of Tyler's M-Estimator for Robust Subspace Recovery
Gilad Lerman, Teng Zhang
Robust Subspace Recovery (RSR) aims to identify an underlying d-dimensional subspace from a dataset heavily corrupted by outliers. Complexity-theoretic results establish a threshol…
Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
Gilad Lerman, Kang Li, Tyler Maunu +1
Robust subspace estimation is fundamental to many machine learning and data analysis tasks. Iteratively Reweighted Least Squares (IRLS) is an elegant and empirically effective appr…
Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator
Gilad Lerman, Teng Zhang
This work analyzes the subspace-constrained Tyler's estimator (STE), a method designed to recover a low-dimensional subspace from a dataset that may be heavily corrupted by outlier…
Improved Convergence Factor of Windowed Anderson Acceleration for Symmetric Fixed-Point Iterations
Casey Garner, Gilad Lerman, Teng Zhang
This paper studies the commonly utilized windowed Anderson acceleration (AA) algorithm for fixed-point methods, . It provides the first proof that when the op…
A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion
Feng Yu, Teng Zhang, Gilad Lerman
We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers. STE is a…