New primal SVM solver with linear computational cost for big data classifications

Support Vector Machines (SVM) is among the most popular classification techniques in ma-chine learning, hence designing fast primal SVM algorithms for large-scale datasets is a hot topic in recent years. This paper presents a new L2- norm regularized primal SVM solver using Augmented Lagrange Multipliers, with linear computational cost for Lp-norm loss functions. The most computationally intensive steps (that determine the algorithmic complexity) of the proposed algorithm is purely and simply matrix-by- vector multiplication, which can be easily parallelized on a multi-core server for parallel computing. We implement and integrate our algorithm into the interfaces and framework of the well-known LibLinear software toolbox. Experiments show that our algorithm is with stable performance and on average faster than the state- of-the-art solvers such as SVM^perf, Pegasos and the LibLinear that integrates the TRON, PCD and DCD algorithms.