Minimum distribution support vector clustering

Yan Wang, Jiali Chen, Xuping Xie, Sen Yang, Wei Pang, Lan Huang, Shuangquan Zhang, Shishun Zhao

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Abstract

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.

Original languageEnglish
Article number1473
JournalEntropy
Volume23
Issue number11
DOIs
Publication statusPublished - 8 Nov 2021

Keywords

  • Dual coordinate descent
  • Margin theory
  • Mean
  • Support vector clustering
  • Variance

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering

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