Pegasos: primal estimated sub-gradient solver for SVM

• Published in: Mathematical programming Vol. 127; no. 1; pp. 3 - 30
• Main Authors: Shalev-Shwartz, Shai, Singer, Yoram, Srebro, Nathan, Cotter, Andrew
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.03.2011; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Bias; Calculus of Variations and Optimal Control; Optimization; Combinatorics; Datasets; Decomposition; Full Length Paper; Iterative methods; Kernels; Learning; Mathematical analysis; Mathematical and Computational Physics; Mathematical Methods in Physics; Mathematical programming; Mathematics; Mathematics and Statistics; Mathematics of Computing; Methods; Numerical Analysis; Optimization; Solvers; Stochastic models; Studies; Support vector machines; Theoretical; Training; More; SVM; Stochastic gradient descent; First; Second
• ISSN: 0025-5610; 1436-4646
• DOI: 10.1007/s10107-010-0420-4

We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy is , where each iteration operates on a single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/ λ , where λ is the regularization parameter of SVM. For a linear kernel, the total run-time of our method is , where d is a bound on the number of non-zero features in each example. Since the run-time does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non-linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an order-of-magnitude speedup over previous SVM learning methods.