Clustering via diffusion adaptation over networks

Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when all agents share the same objective or belong to the same group. However, if agents belong to different clusters or are interested in different objectives, then cooperation can be damaging. In this work, we devise an adaptive combination rule that allows agents to learn which neighbors belong to the same cluster and which other neighbors should be ignored. In doing so, the resulting algorithm enables the agents to identify their grouping and to attain improved learning and estimation performance over networks.


Published in:
3rd International Workshop on Cognitive Information Processing (CIP), 1-6
Presented at:
3rd International Workshop on Cognitive Information Processing (CIP), Baiona, Spain, May 28-30, 2012
Year:
2012
Publisher:
IEEE
Laboratories:




 Record created 2017-12-19, last modified 2018-03-17


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