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Ramp-based twin support vector clustering

  • Zhen Wang
  • Xu Chen
  • Yuan-Hai ShaoEmail author
  • Chun-Na Li
Original Article
  • 38 Downloads

Abstract

Traditional plane-based clustering methods measure the within-cluster or between-cluster scatter by linear, quadratic or some other unbounded functions, which are sensitive to the samples far from the cluster center. This paper introduces the ramp functions into plane-based clustering and proposes a ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is very robust to the samples far from the cluster center, because its within-cluster and between-cluster scatters are measured by the bounded ramp functions. Thus, it is easier to find the intrinsic clusters than other plane-based clustering methods. The nonconvex programming problem in RampTWSVC is solved efficiently through an alternating iteration algorithm, and its local solution can be obtained in a finite number of iterations theoretically. In addition, its nonlinear manifold clustering formation is also proposed via a kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.

Keywords

Clustering Plane-based clustering Ramp function Twin support vector clustering Nonconvex programming 

Notes

Acknowledgements

This work is supported in part by National Natural Science Foundation of China (Nos. 61966024, 11501310, 61866010, 11871183 and 61703370), in part by Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (No. NJYT-19-B01), in part by Natural Science Foundation of Hainan Province (No. 118QN181), in part by Scientific Research Foundation of Hainan University (No. kyqd(sk)1804) and in part by Zhejiang Provincial Natural Science Foundation of China (No. LQ17F030003).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematical SciencesInner Mongolia UniversityHohhotPeople’s Republic of China
  2. 2.School of ManagementHainan UniversityHaikouPeople’s Republic of China

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