Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression
Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, Un-Gu Kang · 2014
How can we scale-up logistic regression, or L1 regularized loss minimization in general, for Terabyte-scale data which do not fit in the memory? How to design the distributed algorithm efficiently? Although there exist two major algorithms for logistic regression, namely Stochastic Gradient Descent (SGD) and Stochastic Coordinate Descent (SCD), they face limitations in distributed environments. Distributed SGD enables data parallelism (i.e., different machines access different part of the input data), but it does not allow feature parallelism (i.e., different machines compute different subsets of the output), and thus the communication cost is high. On the other hand, Distributed SCD allows feature parallelism, but it does not allow data parallelism and thus is not suitable to work in distributed environments.