Clustering via diffusion adaptation over networks

Author

Zhao, Xiaochuan ; Sayed, Ali H.

Author_Institution

Dept. of Electr. Eng., Univ. of California, Los Angeles, CA, USA

fYear

2012

fDate

28-30 May 2012

Firstpage

1

Lastpage

6

Abstract

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.

Keywords

learning (artificial intelligence); multi-agent systems; optimisation; pattern clustering; adaptive combination rule; agent clustering; agent group identification; agent learning; combination weight; diffusion adaptation; distributed processing; in-network processing; neighboring agent cooperation; optimization problem; Adaptation models; Conferences; Least squares approximation; Matrices; Noise; Steady-state; Vectors; Diffusion adaptation; clustering; combination weights; diffusion LMS; energy conservation;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Information Processing (CIP), 2012 3rd International Workshop on

Conference_Location

Baiona

Print_ISBN

978-1-4673-1877-8

Type

conf

DOI

10.1109/CIP.2012.6232902

Filename

6232902