NDLI: Robust Regularized Kernel Regression

Content Provider	IEEE Xplore Digital Library
Author	Jianke Zhu Hoi, S. Lyu, M.R.-T.
Copyright Year	1996
Abstract	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.
Page Count	6
File Size	296559
Starting Page	1639
Ending Page	1644
File Format	PDF
ISSN	10834419
Volume Number	38
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Noise robustness Support vector machines Resonance light scattering Least squares approximation Data mining History Statistics Mathematics Least squares methods support vector machine (SVM) Kernel regression regularized least squares (RLS) robust estimator
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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