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arXiv · 1812.03710

Ramp-based Twin Support Vector Clustering

Zhen Wang·Xu Chen·Chun-Na Li·Yuan-Hai Shao
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Abstract

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more robust because of its boundness, and thus it is more easier to find the intrinsic clusters than other plane-based clustering methods. The non-convex 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, the nonlinear manifold-based formation of RampTWSVC is also proposed by kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.

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Zhen Wang, Xu Chen, Chun-Na Li, Yuan-Hai Shao. 2018-12-10. Ramp-based Twin Support Vector Clustering. https://doi.org/10.1007/s00521-019-04511-3

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