Title :
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
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;
Conference_Titel :
Cognitive Information Processing (CIP), 2012 3rd International Workshop on
Conference_Location :
Baiona
Print_ISBN :
978-1-4673-1877-8
DOI :
10.1109/CIP.2012.6232902
