Robust Parametric Twin Support Vector Machine and Its Application in Human Activity Recognition

This paper proposes a novel and Robust Parametric Twin Support Vector Machine (RPTWSVM) classifier to deal with the heteroscedastic noise present in the human activity recognition framework. Unlike Par-nu-SVM, RPTWSVM proposes two optimization problems where each one of them deals with the structural information of the corresponding class in order to control the effect of heteroscedastic noise on the generalization ability of the classifier. Further, the hyperplanes so obtained adjust themselves in order to maximize the parametric insensitive margin. The efficacy of the proposed framework has been evaluated on standard UCI benchmark datasets. Moreover, we investigate the performance of RPTWSVM on human activity recognition problem. The effectiveness and practicability of the proposed algorithm have been supported with the help of experimental results.