Linex-RSVM: Ramp Linex Support Vector Machine
Chunhua Zhang, Ziyang Wang · Procedia Computer Science · 2022
We propose a novel support vector machine named Ramp Linex Support Vector Machine (Linex-RSVM) for classification problems. By introducing the ramp Linex loss function, the Linex-RSVM has several advantages compared with existing SVMs. First of all, the Ramp Linex loss function is bounded so that the Linex-RSVM can avoid the influence of outliers while trading. Secondly, Linex-RSVM has the sparseness, which is the most essential characteristic of C-SVM, because of the introduction of ∈-insensitive region. Lastly, the Linex-RSVM treats instances differently based on the location of each point, it gives heavier penalty to the instances between the two support hyperplanes and gives lighter penalty to other instances. We adapt the Concave-Convex Procedure (CCCP) to solve the non-convex and non-differential optimization problem in Linex-RSVM and demonstrate effectiveness on numerical experiments.