Robust computation of linear models by convex relaxation

18 Feb 2012 Gilad Lerman Michael McCoy Joel A. Tropp Teng Zhang

Consider a dataset of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers. This work describes a convex optimization problem, called REAPER, that can reliably fit a low-dimensional model to this type of data... (read more)

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