| Autor: |
Zhang, Gong-Duo, Zhao, Shen-Yi, Gao, Hao, Li, Wu-Jun |
| Rok vydání: |
2018 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
