Plane-based clustering with asymmetric distribution loss

Abstract

Ramp-based twin support vector clustering (RampTWSVC) is a powerful clustering method, which measures the within-cluster and between-cluster scatter by the bounded ramp functions. However, the ramp loss is a symmetric function and it does not consider the data distribution, which makes the RampTWSVC not robust enough to deal with noisy datasets. In this paper, we construct an asymmetric distribution loss function and propose a novel plane-based clustering with asymmetric distribution loss (ADPC). The asymmetric distribution loss function combined the ramp loss with first-order and second-order statistics, it inherits the advantages of ramp loss function and captures the data distribution precisely, which makes ADPC more robust to the samples far from the cluster center plane. Moreover, two adjusting parameters t1 and t2 are introduced to flexibly control the form of the asymmetric distribution loss function and improve the performance of the algorithm. In addition, its nonlinear clustering formation is also provided by using the kernel trick. Some experiments on noise-corrupted benchmark UCI and artificial datasets have been provided, and the detailed comparison results show that the proposed ADPC has better performance on most datasets. Further experiments and analysis on the face clustering have been done to demonstrate the effectiveness and feasibility of the proposed ADPC.