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Linear Robust Regression (LRR) seeks to find the parameters of a linear mapping from noisy data corrupted from outliers, such that the number of inliers (i.e. pairs of points where the fitting error of the model is less than a given bound) is maximized. While this problem is known to be NP hard, several tractable relaxations have been recently proposed along with theoretical conditions guaranteeing exact recovery of the parameters of the model. However, these relaxations may perform poorly indoi:10.1109/cvpr.2015.7298946 dblp:conf/cvpr/WangDSC15 fatcat:2ypq4vki4nh33al4e5rrmftqfa