Alternating Relaxed Twin Bounded Support Vector Clustering

Abstract

Maximum margin clustering (MMC) and its improved version are based on the spirit of support vector machine, which will inevitably lead to prohibitively computational complexity when these learning models are encountered with an enormous amount of patterns. To accelerate the clustering efficiency, we propose alternating twin bounded support vector clustering to decompose the original large problem in MMC and its variants into two smaller sized ones, in which solving expensive semi-definite programming is avoided by performing alternating optimization between cluster-specific model parameters and instance-specific labeling assignments. Also the structural risk minimization principle is implemented to obtain good generalization. Additionally, in order to avoid premature convergence, a relaxed version of our algorithm is proposed, in which the hinge loss in the original twin bounded support vector machine is replaced with the Laplacian loss. These two versions can be easily extended to the nonlinear context via kernel tricks. To investigate the efficacy of our clustering algorithm, several experiments are conducted on a number of synthetic and real-world datasets. Experimental results demonstrate that the proposed method has better performance than other existing clustering approaches in terms of clustering accuracy and time efficiency and also possesses the powerful ability to process larger-scaled datasets.