A Polynomial-time Form of Robust Regression
Yu, Yao-liang, Aslan, Özlem, Schuurmans, Dale
–Neural Information Processing Systems
Despite the variety of robust regression methods that have been developed, current regression formulations are either NP-hard, or allow unbounded response to even a single leverage point. We present a general formulation for robust regression --Variational M-estimation--that unifies a number of robust regression methods while allowing a tractable approximation strategy. We develop an estimator that requires only polynomial-time, while achieving certain robustness and consistency guarantees. An experimental evaluation demonstrates the effectiveness of the new estimation approach compared to standard methods.
Neural Information Processing Systems
Dec-31-2012