Robust Regularized Kernel Regression
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual form, which is then solved by some quadratic program solver consequently. In this correspondence, we propose a new formulation for robust regularized kernel regression under the theoretical framework of regularization networks and then tackle the optimization problem directly in the primal. We show that the primal and dual approaches are equivalent to achieving similar regression performance, but the primal formulation is more efficient and easier to be implemented than the dual one. Different from previous work, our approach also optimizes the bias term. In addition, we show that the proposed solution can be easily extended to other noise-reliable loss function, including the Huber-epsiv insensitive loss function. Finally, we conduct a set of experiments on both artificial and real data sets, in which promising results show that the proposed method is effective and more efficient than traditional approaches.
Kernel regression, regularized least squares (RLS), robust estimator, support vector machine (SVM)
Databases and Information Systems
IEEE Transactions on Systems, Man and Cybernetics, Part B (TSMC)
Zhu, Jianke; HOI, Steven; and Lyu, Michael R..
Robust Regularized Kernel Regression. (2008). IEEE Transactions on Systems, Man and Cybernetics, Part B (TSMC). 38, (6), 1639-1644. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2316
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