@Article{Supelec390,
author = {Hervé Frezza-Buet},
title = {{Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network}},
journal = {Neurocomputing},
year = {2008},
volume = {71},
number = {7-9},
pages = {1191-1202},
month = {mar},
url = {http://dx.doi.org/10.1016/j.neucom.2007.12.024},
doi = {10.1016/j.neucom.2007.12.024},
abstract = {In this paper, an original method extended from growing neural
gas (GNG-T) [B. Fritzke, A growing neural gas network learns
topologies, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.),
Advances in Neural Information Processing Systems 7, MIT Press,
Cambridge, MA, 1995, pp. 625–632] is presented. The method
performs continuously vector quantization over a distribution
that changes over time. It deals with both sudden changes and
continuous ones, and is thus suited for the video tracking
framework, where continuous tracking is required as well as fast
adaptation to incoming and outgoing people. The central
mechanism
relies on the management of the quantization resolution, that
copes with stopping condition problems of usual GNG inspired
methods. Application to video tracking is presented.}
}