Compression and machine learning: a new perspective on feature space vectors

@article{Sculley2006CompressionAM,
  title={Compression and machine learning: a new perspective on feature space vectors},
  author={D. Sculley and Carla E. Brodley},
  journal={Data Compression Conference (DCC'06)},
  year={2006},
  pages={332-341}
}
The use of compression algorithms in machine learning tasks such as clustering and classification has appeared in a variety of fields, sometimes with the promise of reducing problems of explicit feature selection. The theoretical justification for such methods has been founded on an upper bound on Kolmogorov complexity and an idealized information space. An alternate view shows compression algorithms implicitly map strings into implicit feature space vectors, and compression-based similarity… CONTINUE READING
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