Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion
Joint Authors
Wang, Kuaini
Zhong, Ping
Pei, Huimin
Ding, Xiaoshuai
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Kuaini& Pei, Huimin& Ding, Xiaoshuai& Zhong, Ping. 2019. Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion. Scientific Programming،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1210751
Modern Language Association (MLA)
Wang, Kuaini…[et al.]. Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion. Scientific Programming No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1210751
American Medical Association (AMA)
Wang, Kuaini& Pei, Huimin& Ding, Xiaoshuai& Zhong, Ping. Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1210751
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210751