Conference proceeding
Generalized Distributed Dual Coordinate Ascent in a Tree Network for Machine Learning
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2019-, pp.3512-3516
05/2019
DOI: 10.1109/ICASSP.2019.8682185
Abstract
With explosion of data size and limited storage space at a single location, data are often distributed at different locations. We thus face the challenge of performing large-scale machine learning from these distributed data through communication networks. In this paper, we generalize the distributed dual coordinate ascent in a star network to a general tree structured network, and provide the convergence rate analysis of the general distributed dual coordinate ascent. In numerical experiments, we demonstrate that the performance of the distributed dual coordinate ascent in a tree network can outperform that of the distributed dual coordinate ascent in a star network when a network has a lot of communication delays between the center node and its direct child nodes.
Details
- Title: Subtitle
- Generalized Distributed Dual Coordinate Ascent in a Tree Network for Machine Learning
- Creators
- Myung Cho - North Carolina State UniversityLifeng Lai - University of California, DavisWeiyu Xu - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2019-, pp.3512-3516
- Publisher
- IEEE
- DOI
- 10.1109/ICASSP.2019.8682185
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Language
- English
- Date published
- 05/2019
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197458302771
Metrics
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