In multiple classification, there is a type of commonproblems where each instance is associated with an ordinal label, which arises in various settings such as text mining, visual recognition and other information retrieval tasks. The support vectorordinal regression (SVOR) is a good model widely used for ordinalregression. In some applications such as document classification, data usually appears in a high dimensional feature space andlinear SVOR becomes a good choice. In this work, we developan efficient solver for training large-scale linear SVOR basedon alternating direction method of multipliers(ADMM). Whencompared empirically on benchmark data sets, the proposedsolver enjoys advantages in terms of both training speed andgeneralization performance over the method based on SMO, which invalidate the effectiveness and efficiency of our algorithm.
['Yong Shi', 'Huadong Wang', 'Lingfeng Niu']
Large-Scale Linear Support Vector Ordinal Regression Solver