### Abstract

The adaptation rule of Vector Quantization algorithms, and consequently the convergence of the generated sequence, depends on the existence and properties of a function called the energy function, defined on a topological manifold. Our aim is to investigate the conditions of existence of such a function for a class of algorithms including the well-known 'K-means' and 'Self-Organizing Map' algorithms. The results presented here extend several previous studies and show that the energy function is not always a potential but at least the uniform limit of a series of potential functions which we call a pseudo-potential. It also shows that a large number of existing vector quantization algorithms developed by the Artificial Neural Networks community fall into this class. The framework we define opens the way to studying the convergence of all the corresponding adaptation rules at once, and a theorem gives promising insights in that direction.

Original language | English |
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Pages (from-to) | 621-630 |

Number of pages | 10 |

Journal | Neural Networks |

Volume | 20 |

Issue number | 5 |

DOIs | |

Publication status | Published - 1 Jul 2007 |

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### Keywords

- Energy function
- K-means
- Neural-gas
- Potential function
- Pseudo-potential
- Self-organizing maps
- Vector quantization

### ASJC Scopus subject areas

- Cognitive Neuroscience
- Artificial Intelligence

### Cite this

*Neural Networks*,

*20*(5), 621-630. https://doi.org/10.1016/j.neunet.2006.11.006