2017/09/02 by Jineng Ren, Ren, Jineng, Jarvis Haupt +1
Computer Science · Engineering · Mathematics · #Face and Expression Recognition #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #cs.LG #math.OC #stat.ML
paper · pdf · doi:10.48550/arxiv.1709.00537
arxiv created 2017/09/09 · arxiv updated 2017/09/12
We propose a communicationally and computationally efficient algorithm for high-dimensional distributed sparse learning. At each iteration, local machines compute the gradient on local data and the master machine solves one shifted l1 regularized minimization problem. The communication cost is reduced from constant times of the dimension number for the state-of-the-art algorithm to constant times of the sparsity number via Two-way Truncation procedure. Theoretically, we prove that the estimation error of the proposed algorithm decreases exponentially and matches that of the centralized method under mild assumptions. Extensive experiments on both simulated data and real data verify that the proposed algorithm is efficient and has performance comparable with the centralized method on solving high-dimensional sparse learning problems.