Learning in diffusion networks with an adaptive projected subgradient method

Authors:
Renato L. G. Cavalcante;Isao Yamada;Bernard Mulgrew
Affiliations:
The University of Edinburgh, Digital Communications Research Institute, Joint Research Institute for Signal and Image Processing, EH9 3JL, UK;Tokyo Institute of Technology, Department of Communications and Integrated Systems, 152-8550, Japan;The University of Edinburgh, Digital Communications Research Institute, Joint Research Institute for Signal and Image Processing, EH9 3JL, UK
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

We present an algorithm that minimizes asymptotically a sequence of non-negative convex functions over diffusion networks. To account for possible node failures, position changes, and/or reachability problems (because of moving obstacles, jammers, etc), the algorithm can cope with dynamic networks and cost functions, a desirable feature for online algorithms where information arrives sequentially. Many projection-based algorithms can be straightforwardly extended to diffusion networks with the proposed scheme. We use the acoustic source localization problem in sensor networks as an example of a possible application.