The similarity metric

@article{Li2003TheSM,
  title={The similarity metric},
  author={Ming Li and Xin Chen and Xin Li and Bin Ma and Paul M. B. Vit{\'a}nyi},
  journal={IEEE Transactions on Information Theory},
  year={2003},
  volume={50},
  pages={3250-3264}
}
A new class of metrics appropriate for measuring effective similarity relations between sequences, say one type of similarity per metric, is studied. We propose a new "normalized information distance", based on the noncomputable notion of Kolmogorov complexity, and show that it minorizes every metric in the class (that is, it is universal in that it discovers all effective similarities). We demonstrate that it too is a metric and takes values in [0, 1]; hence it may be called the similarity… CONTINUE READING
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