On the Equivalence between the Support Vector Machine for Classiication and Sparsiied Fisher's Linear Discriminant
We show that the orientation and location of the separating hyperplane for 2-class supervised pattern classi cation obtained by the Support Vector Machine (SVM) proposed by Vapnik and his colleagues, is equivalent to the solution obtained by Fisher's Linear Discriminant on the set of Support Vectors. In other words, SVM can be seen as a way to \sparsify" Fisher's Linear Discriminant in order to obtain the most generalizing classi cation from the training set.