文献类型：期刊文献

英文题名：Privileged Multi-Target Support Vector Regression

作者：Wu, Guoqiang[1,3]; Tian, Yingjie[2,3]; Liu, Dalian[4]

第一作者：Wu, Guoqiang

机构：[1] School of Computer and Control Engineering, University of Chinese Academy of Sciences, China; [2] Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China; [3] Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China; [4] Department of Basic Course Teaching, Beijing Union University, China

第一机构：School of Computer and Control Engineering, University of Chinese Academy of Sciences, China

年份：2018

卷号：2018-August

起止页码：385-390

外文期刊名：Proceedings - International Conference on Pattern Recognition

收录：EI(收录号：20190206369288)

语种：英文

外文关键词：Computer vision - Learning systems

摘要：Multi-target regression is the problem where each instance is associated with multiple continuous target outputs simultaneously. Its major challenges arise with jointly exploring the complex input-output relationships and inter-target correlations. One representative approach is to build many independent single-target Support Vector Regression (SVR) models for each output target which can capture complex input-output relationships via the kernel trick. However, it does not involve inter-target correlations to improve the performance. Meanwhile, there are also many regularization-based methods which mainly explore the linear inter-target correlations, e.g., a low-rank constraint on the parameter matrix. However, in practice, it might be restrictive to assume the targets to be linearly related, and allowing for nonlinear relationships is a challenge. Motivated by Learning Using Privileged Information (LUPI), we propose a novel privileged multi-target support vector regression (MT-PSVR) model which can jointly explore the complex input-output relationships and nonlinear inter-target correlations. It explicitly explores inter-target correlations by viewing other targets as privileged information when training each target model. Besides, it can naturally use the kernel trick to explore both the complex input-output relationships and nonlinear inter-target correlations. Experimental results on many benchmark datasets validate the effectiveness of our approach. ? 2018 IEEE.