PTSVRs: Regression models via projection twin support vector machine

Taking motivation from projection twin support vector machine (PTSVM) formulation for recognition, this paper proposes two novel projection twin support vector regression (PTSVR) models, called pair-shifted PTSVR (PPTSVR) and single-shifted PTSVR (SPTSVR), respectively. PTSVRs construct indirectly the target regressor by two functions (hyperplanes) obtained from two smaller-sized quadratic programming problems (QPPs), in which each normal direction makes the within-class variance of the projection of shifted set (or original set) be minimized and the projected center be at a distance of at least 1 from the projection of the other shifted set. As other twin support vector machine (TWSVM) models, the learning speed of PTSVRs is faster than classical support vector regression (SVR) since each of their QPP has only half size. Experimental results on several synthetic as well as benchmark datasets indicate the significant advantage of PPTSVR and SPTSVR in the generalization performance.