Linear classification has demonstrated success in many areas of applications. Modern algorithms for linear classification can train reasonably good models while going through the data in only tens of rounds. However, large data often does not fit in the memory of a single machine, which makes the bottleneck in large-scale learning the disk I/O, not the CPU. Following this observation, Yu et al. (2010) made significant progress in reducing disk usage, and their algorithms now outperform LIBLINEAR. In this paper, rather than optimizing algorithms on a single machine, we propose and implement distributed algorithms that achieve parallel disk loading and access the disk only once. Our large-scale learning algorithms are based on the framework of alternating direction methods of multipliers. The framework derives a subproblem that remains to be solved efficiently for which we propose using dual coordinate descent and trust region Newton method. Our experimental evaluations on large datasets demonstrate that the proposed algorithms achieve significant speedup over the classifier proposed by Yu et al. running on a single machine. Our algorithms are faster than existing distributed solvers, such as Zinkevich et al. (2010)’s parallel stochastic gradient descent and Vowpal Wabbit.
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