Multi-label support vector machine Rank-SVM is an effective algorithm for multi-label classification, which is formulated as a quadratic programming problem with q equality constraints and lots of box constraints for a q-class multi-label data set. So far, Rank-SVM is solved by Frank-Wolfe method FWM, where a large-scale linear programming problem needs to be dealt with at each iteration. In this paper, we propose a random block coordinate descent method RBCDM for Rank-SVM, in which a small-scale quadratic programming problem with at least q+1 variables randomly is solved at each iteration. Experiments on three data sets illustrate that our RBCDM runs much faster than FWM for Rank-SVM, and Rank-SVM is a powerful candidate for multi-label classification.
['Jianhua Xu']
A Random Block Coordinate Descent Method for Multi-label Support Vector Machine