Magnification control for batch neural gas

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Magnification control for batch neural gas
Neural gas (NG) constitutes a very robust clustering algorithm which can be derived as stochastic gradient descent from a cost function closely connected to the quantization error. In the limit, an NG network samples the underlying data distribution. Thereby, the connection is not linear, rather, it follows a power law with magnification exponent different from the information theoretically optimum one in adaptive map formation. There exists a couple of schemes to explicitely control the exponent such as local learning which leads to a small change of the learning algorithm of NG. Batch NG constitutes a fast alternative optimization scheme for NG vector quantizers which has been derived from the same cost function and which constitutes a fast Newton optimization scheme. It possesses the same magnification factor (different from 1) as standard online NG. In this paper, we propose a method to integrate magnification control by local learning into batch NG. Thereby, the key observation i...
Barbara Hammer, Alexander Hasenfuss, Thomas Villma
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Authors Barbara Hammer, Alexander Hasenfuss, Thomas Villmann
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