Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification
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We develop efficient algorithms to train ℓ_1-regularized linear classifiers with large dimensionality d of the feature space, number of classes k, and sample size n. Our focus is on a special class of losses that includes, in particular, the multiclass hinge and logistic losses. Our approach combines several ideas: (i) passing to the equivalent saddle-point problem with a quasi-bilinear objective; (ii) applying stochastic mirror descent with a proper choice of geometry which guarantees a favorable accuracy bound; (iii) devising non-uniform sampling schemes to approximate the matrix products. In particular, for the multiclass hinge loss we propose a sublinear algorithm with iterations performed in O(d+n+k) arithmetic operations.
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