Batch and median neural gas
摘要
Neural Gas (NG) constitutes a very robust clustering algorithm given euclidian data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.
引用
@article{arxiv.math/0610561,
title = {Batch and median neural gas},
author = {Marie Cottrell and Barbara Hammer and Alexander Hasenfuss and Thomas Villmann},
journal= {arXiv preprint arXiv:math/0610561},
year = {2007}
}
备注
In Special Issue after WSOM 05 Conference, 5-8 september, 2005, Paris