Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications

11 Jan 2017Xiaowei ZhangChi XuYu ZhangTingshao ZhuLi Cheng

This paper studies the problem of multivariate linear regression where a portion of the observations is grossly corrupted or is missing, and the magnitudes and locations of such occurrences are unknown in priori. To deal with this problem, we propose a new approach by explicitly consider the error source as well as its sparseness nature... (read more)

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