Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms

@article{Karayiannis2005SoftLV,
  title={Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms},
  author={Nicolaos B. Karayiannis and Mary M. Randolph-Gips},
  journal={IEEE transactions on neural networks},
  year={2005},
  volume={16 2},
  pages={423-35}
}
This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative… CONTINUE READING
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