Clustering by twin support vector machine and least square twin support vector classifier with uniform output coding

Abstract

The recently proposed twin support vector clustering (TWSVC) is a powerful clustering method. However, TWSVC may encounter the singularity problem and it is time consuming in the learning stage. In this paper, we introduce some efficient techniques into TWSVC, and propose two clustering models, called twin bounded support vector clustering (TBSVC) and least square twin bounded support vector clustering (LSTBSVC), respectively. TBSVC introduces a maximum margin regularization term into TWSVC, which not only avoids its singularity but also significantly improves the performance. LSTBSVC introduces the least square formation into TBSVC to greatly accelerate its learning speed. Moreover, a uniform output coding for LSTBSVC is introduced to cope with the non-uniformed problem in the learning procedures. In addition, nonlinear clustering is also extended to the above clustering methods by using the kernel trick. Experimental results show the effectiveness and efficiency of our methods.