Privileged Multi-Target Support Vector Regression
Guoqiang Wu, Yingjie Tian, Dalian Liu · 2018
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.