Robust least squares support vector machine based on recursive outlier elimination |
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Authors: | Wen Wen Zhifeng Hao Xiaowei Yang |
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Affiliation: | (1) School of Computer, Guangdong University of Technology, 510006 Guangzhou, China;(2) School of Mathematical Science, South China University of Technology, 510641 Guangzhou, China |
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Abstract: | To achieve robust estimation for noisy data set, a recursive outlier elimination-based least squares support vector machine
(ROELS-SVM) algorithm is proposed in this paper. In this algorithm, statistical information from the error variables of least
squares support vector machine is recursively learned and a criterion derived from robust linear regression is employed for
outlier elimination. Besides, decremental learning technique is implemented in the recursive training–eliminating stage, which
ensures that the outliers are eliminated with low computational cost. The proposed algorithm is compared with re-weighted
least squares support vector machine on multiple data sets and the results demonstrate the remarkably robust performance of
the ROELS-SVM. |
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Keywords: | |
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