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 formula...
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sg-smu-ink.sis_research-33162020-04-01T02:11:46Z Robust regularized Kernel regression ZHU, Jianke HOI, Steven C. H. LYU, Michael R. 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. 2008-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2316 info:doi/10.1109/TSMCB.2008.927279 https://ink.library.smu.edu.sg/context/sis_research/article/3316/viewcontent/RobustRegularized_2008.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Kernel regression regularized least squares (RLS) robust estimator support vector machine (SVM) Databases and Information Systems |
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Kernel regression regularized least squares (RLS) robust estimator support vector machine (SVM) Databases and Information Systems ZHU, Jianke HOI, Steven C. H. LYU, Michael R. Robust regularized Kernel regression |
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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. |
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ZHU, Jianke HOI, Steven C. H. LYU, Michael R. |
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ZHU, Jianke HOI, Steven C. H. LYU, Michael R. |
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ZHU, Jianke |
title |
Robust regularized Kernel regression |
title_short |
Robust regularized Kernel regression |
title_full |
Robust regularized Kernel regression |
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Robust regularized Kernel regression |
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Robust regularized Kernel regression |
title_sort |
robust regularized kernel regression |
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Institutional Knowledge at Singapore Management University |
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2008 |
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https://ink.library.smu.edu.sg/sis_research/2316 https://ink.library.smu.edu.sg/context/sis_research/article/3316/viewcontent/RobustRegularized_2008.pdf |
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