Multi-output LS-SVR machine in extended feature space

Abstract

Support Vector Regression machine is usually used to predict a single output. Previous multi-output regression problems are dealt with by building up multiple independent single-output regression models. Taking into account the correlations between multi-outputs, a new method for constructing a multi-output model directly is presented. By extending the original feature space using the method of vector virtualization, the multi-output case is expressed as a formally equivalent single-output one in the extended feature space, which can be solved with least square support vector regression machines. Experimental results show that this method presents good performance.