PCA Feature Selection For Recognition of Chatter Gestation

Abstract

A SVM(Support Vector Machine)-like framework provides a novel way to learn linear principal component analysis (PCA), which is easy to be solved and can obtain the unique global solution. SVM is good at classification and PCA features is introduced into SVM. So, a new recognition method based on hybrid PCA and SVM is proposed and used for a series of experiments on chatter gestation. The results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.