Feature-Distributed SVRG for High-Dimensional Linear Classification
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Abstract (arXiv)
Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called feature-distributed stochastic variance reduced gradient (FD-SVRG) for high-dimensional linear classification. Unlike most existing distributed learning methods which are instance-distributed, FD-SVRG is feature-distributed. FD-SVRG has lower communication cost than other instance-distributed methods when the data dimensionality is larger than the number of data instances. Experimental results on real data demonstrate that FD-SVRG can outperform other state-of-the-art distributed methods for high-dimensional linear classification in terms of both communication cost and wall-clock time, when the dimensionality is larger than the number of instances in training data.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2018.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 1.
8 papers.
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