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Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex | IEEE Conference Publication | IEEE Xplore

Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex


Abstract:

Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition...Show More

Abstract:

Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced to the well-known problem of Euclidean projection onto the positive simplex, which we can solve exactly in expected O(k) time, where k is the number of classes. We demonstrate that our method empirically achieves state-of-the-art convergence on several large-scale high-dimensional datasets.
Date of Conference: 24-28 August 2014
Date Added to IEEE Xplore: 06 December 2014
Electronic ISBN:978-1-4799-5209-0
Print ISSN: 1051-4651
Conference Location: Stockholm, Sweden

References

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