Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory

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Guillaume Pouliot ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4133-4140, 2018.

Abstract

The multicategory SVM (MSVM) of Lee et al. (2004) is a natural generalization of the classical, binary support vector machines (SVM). However, its use has been limited by computational difficulties. The simplex-cone SVM (SCSVM) of Mroueh et al. (2012) is a computationally efficient multicategory classifier, but its use has been limited by a seemingly opaque interpretation. We show that MSVM and SCSVM are in fact exactly equivalent, and provide a bijection between their tuning parameters. MSVM may then be entertained as both a natural and computationally efficient multicategory extension of SVM. We further provide a Donsker theorem for finite-dimensional kernel MSVM and partially answer the open question pertaining to the very competitive performance of One-vs-Rest methods against MSVM. Furthermore, we use the derived asymptotic covariance formula to develop an inverse-variance weighted classification rule which improves on the One-vs-Rest approach.

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