Ordinal-Class Core Vector Machine

作     者：顾彬 王建东 李涛 

作者机构：Department of Computer Science and EngineeringNanjing University of Aeronautics and Astronautics College of Electronic and Information EngineeringNanjing University of Information Science and Technology 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2010年第25卷第4期

页      面：699-708页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：supported by the National High-Tech Research and Development 863 Program of China under Grant No. 2006AA12A106 

主　　题：support vector machine ordinal regression ranking learning core vector machine minimum enclosing ball 

摘      要：Ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines （SVMs） have also been proposed for tackling it, but the scalability aspect of these approaches to handle large datasets still needs much of exploration. In this paper, we will extend the recent proposed algorithm Core Vector Machine （CVM） to the ordinal-class data, and propose a new algorithm named as Ordinal-Class Core Vector Machine （OCVM）. Similar with CVM, its asymptotic time complexity is linear with the number of training samples, while the space complexity is independent with the number of training samples. We also give some analysis for OCVM, which mainly includes two parts, the first one shows that OCVM can guarantee that the biases are unique and properly ordered under some situation; the second one illustrates the approximate convergence of the solution from the viewpoints of objective function and KKT conditions. Experiments on several synthetic and real world datasets demonstrate that OCVM scales well with the size of the dataset and can achieve comparable generalization performance with existing SVM implementations.