Solution Path Algorithm for Twin Multi-class Support Vector Machine
This work addresses model selection efficiency for multi-class classification in machine learning, but it is incremental as it builds on existing twin SVM methods.
The paper tackled the twin multi-class support vector machine's slow model selection by developing a fast regularization parameter tuning algorithm, achieving comparable classification performance on nine UCI data sets without solving quadratic programming problems.
The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems. However, it suffers from difficulties in effective solution of multi-classification and fast model selection. This work devotes to the fast regularization parameter tuning algorithm for the twin multi-class support vector machine. Specifically, a novel sample data set partition strategy is first adopted, which is the basis for the model construction. Then, combining the linear equations and block matrix theory, the Lagrangian multipliers are proved to be piecewise linear w.r.t. the regularization parameters, so that the regularization parameters are continuously updated by only solving the break points. Next, Lagrangian multipliers are proved to be 1 as the regularization parameter approaches infinity, thus, a simple yet effective initialization algorithm is devised. Finally, eight kinds of events are defined to seek for the starting event for the next iteration. Extensive experimental results on nine UCI data sets show that the proposed method can achieve comparable classification performance without solving any quadratic programming problem.