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A Support Vector Machine for Regression in Complex Field
Volume 28, Issue 4 (2017), pp. 651–664
Pub. online: 1 January 2017      Type: Research Article      Open accessOpen Access
Received
1 September 2016
Accepted
1 November 2017
Published
1 January 2017

Abstract

In this paper, one method for training the Support Vector Regression (SVR) machine in the complex data field is presented, which takes into account all the information of both the real and imaginary parts simultaneously. Comparing to the existing methods, it not only considers the geometric information of the complex-valued data, but also can be trained with the same amount of computation as the original SVR in the real data field. The accuracy of the proposed method is analysed by the simulation experiments. This also can be applied to the field of anti-interference for satellite navigation successfully, which shows its effectiveness in practical application.

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Biographies

Lang Rongling
ronglinglang@163.com

R. Lang is currently an associate professor in the school of Electronic and Information Engineering in Beihang University. She got BSc Degree and MSc Degree in applied mathematics, Northwestern Polytechnical University, P.R. China, in 2002. She received PhD degree in automatic control, Northwestern Polytechnical University, Xi’an, P.R. China, in 2005. Currently, her research area are data driven GNSS signal monitoring, fault diagnosis and fault prognosis.

Zhao Fei
zhaofei9307@163.com

F. Zhao is currently pursuing his MS degree in communication and information engineering in Beihang University, Beijing, China. He received his MS degree in communication engineering from China University of Geosciences, Beijing, China. He is now doing the research on jamming suppression in satellite navigation system, blind signal processing and spatial spectrum estimation.

Shi Yongtang
shi@nankai.edu.cn

Y. Shi studied mathematics at Northwest University (Xi’an, China) and received his PhD in applied mathematics from Nankai University (Tianjin, China). He visited Technische Universität Bergakademie Freiberg (Germany), UMIT (Austria), Simon Fraser University (Canada) and University of Mississippi (USA). Currently, he is an associate professor at the Center for Combinatorics of Nankai University. His research interests are in graph theory and its applications, especially the applications of graph theory in computer science and information theory. He has written over 60 publications in graph theory and its applications.


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Open access article under the CC BY license.

Keywords
support vector machine for regression complex field kernel function

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