Phase transitions in vector quantization and neural gas
نویسندگان
چکیده
منابع مشابه
Phase transitions in vector quantization and neural gas
The statistical physics of off-learning is applied to Winner-Takes-All and rank-based vector quantization (VQ), including the Neural Gas (NG). The analysis is based on the limit of high training temperatures and the annealed approximation. The typical learning behavior is evaluated for systems of two and three prototypes with data drawn from a mixture of high dimensional Gaussian clusters. The ...
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We study Winner-Takes-All and rank based Vector Quantization along the lines of the statistical physics of off-line learning. Typical behavior of the system is obtained within a model where high-dimensional training data are drawn from a mixture of Gaussians. The analysis becomes exact in the simplifying limit of high training temperature. Our main findings concern the existence of phase transi...
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Various alternatives have been developed to improve the winner-takes-all (WTA) mechanism in vector quantization, including the neural gas (NG). However, the behavior of these algorithms including their learning dynamics, robustness with respect to initialization, asymptotic results, etc. has only partially been studied in a rigorous mathematical analysis. The theory of on-line learning allows f...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2009
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2008.10.023