On the use of self-organizing maps to accelerate vector quantization
<|reference_start|>On the use of self-organizing maps to accelerate vector quantization: Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid. SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algorithm drastically increases the speed of convergence of the vector quantization process. This fact is demonstrated through extensive simulations on artificial and real examples, with specific SOM (fixed and decreasing neighborhoods) and SCL algorithms.<|reference_end|>
@article{de bodt2007on,
    title={On the use of self-organizing maps to accelerate vector quantization},
    author={Eric De Bodt (ESA, Iag-Fin, Dice), Marie Cottrell (MATISSE, Samos),
  Patrick Letr'emy (MATISSE, Samos), Michel Verleysen (DICE)},
    journal={Neurocomputing 56 (2004) 187-203},
    year={2007},
    doi={10.1016/j.neucom.2003.09.009},
    archivePrefix={arXiv},
    eprint={math/0701142},
    primaryClass={math.ST cs.NE stat.TH}
}