A random block coordinate descent method for multi-label support vector machine

Abstract

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. © Springer-Verlag 2013.