Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion

The robustness problem of the classical proximal support vector machine for regression estimation (PSVR) when confronting with samples in the presence of outliers is addressed in this paper. Correntropy is a local similarity measure between two arbitrary variables and has been proven the insensitivi...

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Veröffentlicht in:Scientific programming 2019-01, Vol.2019 (2019), p.1-11
Hauptverfasser: Zhong, Ping, Ding, Xiaoshuai, Pei, Huimin, Wang, Kuaini
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container_title Scientific programming
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creator Zhong, Ping
Ding, Xiaoshuai
Pei, Huimin
Wang, Kuaini
description The robustness problem of the classical proximal support vector machine for regression estimation (PSVR) when confronting with samples in the presence of outliers is addressed in this paper. Correntropy is a local similarity measure between two arbitrary variables and has been proven the insensitivity to noises and outliers. Based on the maximum correntropy criterion (MCC), a correntropy-based robust PSVR framework is proposed, named as RPSVR-MCC. The half-quadratic optimization method is employed to solve the resultant optimization, and an iterative algorithm is developed to solve RPSVR-MCC. In each iteration, the complex optimization can be converted to a linear system of equations which can be easily solved by the widely popular optimization techniques. The experimental results on synthetic datasets and real-world benchmark datasets demonstrate that the effectiveness of the proposed method. Moreover, the superiority of the proposed algorithm is more evident in noisy environment, especially in the presence of outliers.
doi_str_mv 10.1155/2019/7102946
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subjects Algorithms
Artificial intelligence
Criteria
Datasets
International conferences
Iterative algorithms
Linear programming
Noise
Optimization
Optimization techniques
Outliers (statistics)
Pattern recognition
Researchers
Robustness (mathematics)
Signal processing
Support vector machines
title Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion
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