Fast Robust Twin Support Vector Clustering

Abstract

This paper develops a fast k-plane clustering method called L1-norm Distance Minimization based Fast Robust TWSVC (FRTWSVC) by using robust L1-norm distance. To solve the resulted objective, we propose a novel iterative algorithm. Only a system of linear equations needs to be computed in each iteration. These characteristics make our methods more powerful and efficient than TWSVC. We also conduct some insightful analysis on the convergence of the proposed algorithms. Theoretical insights and effectiveness of our method are further supported by promising experimental results.