• Corpus ID: 221892448

Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification.

@article{Zenil2020MinimalAI,
  title={Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification.},
  author={Hector Zenil and Narsis Aftab Kiani and Felipe S. Abrah{\~a}o and Antonio Rueda-Toicen and Allan A. Zea and Jesper Tegn'er},
  journal={arXiv: Data Structures and Algorithms},
  year={2020}
}
We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic probability and information theory designed to minimize the loss of algorithmic information, including a lossless-compression-based lossy compression algorithm. The methods can select and coarse-grain data in an algorithmic-complexity fashion (without the use of popular compression algorithms) by collapsing regions that may procedurally be regenerated from… 

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