Quantifying loss of information in network-based dimensionality reduction techniques

@article{Zenil2016QuantifyingLO,
  title={Quantifying loss of information in network-based dimensionality reduction techniques},
  author={Hector Zenil and Narsis A. Kiani and Jesper Tegn{\'e}r},
  journal={ArXiv},
  year={2016},
  volume={abs/1504.06249}
}
  • Hector Zenil, Narsis A. Kiani, Jesper Tegnér
  • Published 2016
  • Biology, Computer Science, Mathematics
  • ArXiv
  • To cope with the complexity of large networks, a number of dimensionality reduction techniques for graphs have been developed. However, the extent to which information is lost or preserved when these techniques are employed has not yet been clear. Here we develop a framework, based on algorithmic information theory, to quantify the extent to which information is preserved when network motif analysis, graph spectra and spectral sparsification methods are applied to over twenty different… CONTINUE READING

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