Winner-Relaxing Self-Organizing Maps
<|reference_start|>Winner-Relaxing Self-Organizing Maps: A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behaviour is calculated analytically. For the original variant a magnification exponent of 4/7 is derived; the generalized version allows to steer the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional case, thus provides optimal mapping in the sense of information theory. The Winner Relaxing Algorithm requires minimal extra computations per learning step and is conveniently easy to implement.<|reference_end|>
@article{claussen2002winner-relaxing,
    title={Winner-Relaxing Self-Organizing Maps},
    author={Jens Christian Claussen (Theoretical Physics, University Kiel)},
    journal={Neural Computation 17 (5), 996-1009 (2005)},
    year={2002},
    doi={10.1162/0899766053491922},
    archivePrefix={arXiv},
    eprint={cond-mat/0208414},
    primaryClass={cond-mat.dis-nn cs.NE nlin.AO q-bio.NC}
}