March 28, 2024, 4:47 a.m. | Gilad Lerman, Feng Yu, Teng Zhang

stat.ML updates on

arXiv:2403.18658v1 Announce Type: cross
Abstract: This work analyzes the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers. It assumes a weak inlier-outlier model and allows the fraction of inliers to be smaller than a fraction that leads to computational hardness of the robust subspace recovery problem. It shows that in this setting, if the initialization of STE, which is an iterative algorithm, satisfies a certain condition, then STE …

abstract arxiv computational dataset estimator leads low outlier outliers tyler type work

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