Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
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发表时间：
2015-07
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通讯作者：
Ching-pei Lee;D. Roth
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作者：
Ching-pei Lee;D. Roth
训练机器学习模型有时需要在超过单台机器容量的大量数据上完成，这促使最近开发以分布式方式训练的算法的工作。针对大数据量的线性支持向量机的分布式训练问题，提出了一种高效的盒约束二次优化算法。我们的主要技术贡献是为计算每次迭代的最优步长问题提供了一种解析解，使用了一种只需要O(1)通信代价来确保快速收敛的有效方法。对于分布求解非强凸线性支持向量机对偶问题，在这种最优步长下，我们的方法具有全局线性收敛，或者等价地，对于e-精确解具有O(log(1/e))迭代复杂度。实验还表明，我们的方法明显快于目前最先进的分布式线性支持向量机算法，包括DSVM-AVE、DisDCA和Tron。
Training machine learning models sometimes needs to be done on large amounts of data that exceed the capacity of a single machine, motivating recent works on developing algorithms that train in a distributed fashion. This paper proposes an efficient box-constrained quadratic optimization algorithm for distributedly training linear support vector machines (SVMs) with large data. Our key technical contribution is an analytical solution to the problem of computing the optimal step size at each iteration, using an efficient method that requires only O(1) communication cost to ensure fast convergence. With this optimal step size, our approach is superior to other methods by possessing global linear convergence, or, equivalently, O(log(1/e)) iteration complexity for an e-accurate solution, for distributedly solving the non-strongly-convex linear SVM dual problem. Experiments also show that our method is significantly faster than state-of-the-art distributed linear SVM algorithms including DSVM-AVE, DisDCA and TRON.