Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures

@article{Gold1994LearningWP,
  title={Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures},
  author={Steven Gold and Anand Rangarajan and Eric Mjolsness},
  journal={Neural Computation},
  year={1994},
  volume={8},
  pages={787-804}
}
Prior knowledge constraints are imposed upon a learning problem in the form of distance measures. Prototypical 2D point sets and graphs are learned by clustering with point-matching and graph-matching distance measures. The point-matching distance measure is approximately invariant under affine transformationstranslation, rotation, scale, and shearand permutations. It operates between noisy images with missing and spurious points. The graph-matching distance measure operates on weighted graphs… CONTINUE READING

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