Distributed multi-task classification: a decentralized online learning approach

Distributed multi-task classification: a decentralized online learning approach Although dispersing one single task to distributed learning nodes has been intensively studied by the previous research, multi-task learning on distributed networks is still an area that has not been fully exploited, especially under decentralized settings. The challenge lies in the fact that different tasks may have different optimal learning weights while communication through the distributed network forces all tasks to converge to an unique classifier. In this paper, we present a novel algorithm to overcome this challenge and enable learning multiple tasks simultaneously on a decentralized distributed network. Specifically, the learning framework can be separated into two phases: (i) multi-task information is shared within each node on the first phase; (ii) communication between nodes then leads the whole network to converge to a common minimizer. Theoretical analysis indicates that our algorithm achieves a $$\mathcal {O}(\sqrt{T})$$ O ( T ) regret bound when compared with the best classifier in hindsight, which is further validated by experiments on both synthetic and real-world datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Learning Springer Journals

Distributed multi-task classification: a decentralized online learning approach

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computing Methodologies; Simulation and Modeling; Language Translation and Linguistics
ISSN
0885-6125
eISSN
1573-0565
D.O.I.
10.1007/s10994-017-5676-y
Publisher site
See Article on Publisher Site

Abstract

Although dispersing one single task to distributed learning nodes has been intensively studied by the previous research, multi-task learning on distributed networks is still an area that has not been fully exploited, especially under decentralized settings. The challenge lies in the fact that different tasks may have different optimal learning weights while communication through the distributed network forces all tasks to converge to an unique classifier. In this paper, we present a novel algorithm to overcome this challenge and enable learning multiple tasks simultaneously on a decentralized distributed network. Specifically, the learning framework can be separated into two phases: (i) multi-task information is shared within each node on the first phase; (ii) communication between nodes then leads the whole network to converge to a common minimizer. Theoretical analysis indicates that our algorithm achieves a $$\mathcal {O}(\sqrt{T})$$ O ( T ) regret bound when compared with the best classifier in hindsight, which is further validated by experiments on both synthetic and real-world datasets.

Journal

Machine LearningSpringer Journals

Published: Nov 2, 2017

References

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