Adaptive Relevance Matrices in Learning Vector Quantization

@article{Schneider2009AdaptiveRM,
  title={Adaptive Relevance Matrices in Learning Vector Quantization},
  author={Petra Schneider and Michael Biehl and Barbara Hammer},
  journal={Neural Computation},
  year={2009},
  volume={21},
  pages={3532-3561}
}
We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric… CONTINUE READING
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