Balanced Relative Margin Machine -The missing piece between FDA and SVM classification
In Pattern Recognition Letters, Elsevier, volume 41, pages 43-52, May/2014.
In this theoretical work we approach the class of relative margin classification algorithms from the mathematical programming perspective. In particular, we propose a Balanced Relative Margin Machine (BRMM) and then extend it by a 1-norm regularization. We show that this new classifier concept connects Support Vector Machines (SVM) with Fisher’s Discriminant Analysis (FDA) by the insertion of a range parameter. It is also strongly connected to the Support Vector Regression. Using this BRMM it is now possible to optimize the classifier type instead of choosing it beforehand. We verify our findings empirically by means of simulated and benchmark data.
Kernel methods,Linear discriminant analysis,Mathematical programming,Regularization,Support vector machines