Abstract.Recently proposed Multi-weight vector projection support vector machines (MVSVM) is an outstanding algorithm for binary classification. However, it measuring distance in the objective function by squared L2-norm, which is easy to find that the impact of outliers is exaggerated. To alleviate this, we propose an effective algorithm, termed as Robust MVSVM based on the L1-norm distance (L1-MVSVM). The distance in the objective of L1-MVSVM is measured by L1-norm. Besides, we design a powerful iterative algorithm to solve the optimal problem of L1-norm, whose convergence is theoretically ensured. Finally, the effectiveness of L1-MVSVM has been verified through extensive experiments.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.