摘要: Linear subspace has many important applications in computer vision, such as structure from motion, motion estimation, layer extraction, object recognition, and tracking. Singular Value Decomposition (SVD) algorithm is a standard technique to compute the input data. The SVD algorithm, however, sensitive outliers it uses L2 norm metric, can not handle missing data either. In this paper, we propose using L1 metric subspace. We show that robust present two algorithms optimize metric: weighted median quadratic programming algorithm. views conclusions contained document are those of authors should be interpreted representing official policies, either expressed or implied, Carnegie Mellon University U.S. Government.
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