A Support Vector Machine for Regression in Complex Field

Article type: Research Article

Authors: Lang, Rongling¹ | Zhao, Fei¹ | Shi, Yongtang^{2; *}

Affiliations: [1] School of Electronics and Information Engineering, Beihang University, Beijing 100191, China. E-mails: [email protected], [email protected] | [2] Center for Combinatorics and LPMC, Nankai University, Tianjin 300071, China. E-mail: [email protected]

Correspondence: [*] Corresponding author.

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.

Keywords: support vector machine for regression, complex field, kernel function

Journal: Informatica, vol. 28, no. 4, pp. 651-664, 2017