Distance Learning in Discriminative Vector Quantization

@article{Schneider2009DistanceLI,
  title={Distance Learning in Discriminative Vector Quantization},
  author={Petra Schneider and Michael Biehl and Barbara Hammer},
  journal={Neural Computation},
  year={2009},
  volume={21},
  pages={2942-2969}
}
Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods to more general metric structures have been proposed, such as relevance adaptation in generalized LVQ… CONTINUE READING