Title :
Diffusion LMS for multitask problems with overlapping hypothesis subspaces
Author :
Jie Chen ; Richard, Cedric ; Hero, Alfred O. ; Sayed, Ali H.
Author_Institution :
Univ. of Michigan, Ann Arbor, MI, USA
Abstract :
There are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously by networked agents. In this paper, we formulate an online multitask learning problem where node hypothesis spaces partly overlap. A cooperative algorithm based on diffusion adaptation is derived. Some results on its stability and convergence properties are also provided. Simulations are conducted to illustrate the theoretical results.
Keywords :
convergence; learning (artificial intelligence); least mean squares methods; multi-agent systems; convergence properties; diffusion LMS; diffusion adaptation; multiple optimum parameter vectors; networked agents; node hypothesis spaces; online multitask learning problem; overlapping hypothesis subspaces; stability; Adaptive systems; Convergence; Estimation; Least squares approximations; Optimization; Signal processing algorithms; Vectors; Multitask learning; collaborative processing; diffusion strategy; distributed optimization;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on
Conference_Location :
Reims
DOI :
10.1109/MLSP.2014.6958929
