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

  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)},
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
Highly Cited
This paper has 231 citations. REVIEW CITATIONS
51 Citations
23 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 51 extracted citations

232 Citations

Citations per Year
Semantic Scholar estimates that this publication has 232 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 23 references

Similar Papers

Loading similar papers…