Speaker: [http://www.umiacs.umd.edu/~kmitra/ Kaushik Mitra] -- Date: July 21, 2011
Speaker: [http://www.umiacs.umd.edu/~kmitra/ Kaushik Mitra] -- Date: July 21, 2011
−
.
+
Robust regression is a combinatorial optimization problem. Hence, algorithms such as RANSAC and least median squares (LMedS), which are successful in solving low-dimensional problems, can not be used for solving high-dimensional problems. We show that under certain conditions the robust linear regression problem can be solved accurately using polynomial-time algorithms such as a modified version of basis pursuit and a sparse Bayesian algorithm. We then extend our robust formulation to the case of kernel regression, specifically to propose a robust version for relevance vector machine (RVM) regression.