Multiclass Latent Locally Linear Support Vector Machines

  title={Multiclass Latent Locally Linear Support Vector Machines},
  author={Marco Fornoni and Barbara Caputo and Francesco Orabona},
Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, 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 classifiers and the flexibility of non linear ones, a growing, promising alternative is represented by methods that… CONTINUE READING
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