DocumentCode :
984011
Title :
Robust Regularized Kernel Regression
Author :
Zhu, Jianke ; Hoi, Steven C H ; Lyu, Michael Rung-Tsong
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin
Volume :
38
Issue :
6
fYear :
2008
Firstpage :
1639
Lastpage :
1644
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.
Keywords :
quadratic programming; regression analysis; support vector machines; Huber-epsiv insensitive loss function; noise-reliable loss function; optimization problem; quadratic program solver; regularization networks; regularized kernel regression; Data mining; History; Kernel; Least squares approximation; Least squares methods; Mathematics; Noise robustness; Resonance light scattering; Statistics; Support vector machines; Kernel regression; regularized least squares (RLS); robust estimator; support vector machine (SVM); Algorithms; Artificial Intelligence; Computer Simulation; Data Interpretation, Statistical; Models, Statistical; Pattern Recognition, Automated; Regression Analysis;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2008.927279
Filename :
4669534
Link To Document :
بازگشت