Multiclass Latent Locally Linear Support Vector Machines

Kernelized Support Vector Machines (SVM) have gained the status of o-the-shelf classiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classiers and the exibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classiers through local combinations of linear ones. In this paper we propose a new multi class local classier,