# 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…

## 9 Citations

### An Algorithmic Information Distortion in Multidimensional Networks

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### Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

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The results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmmic information of the multid dimensional space itself needs to be taken into account in multiddimensional network complexity analysis.

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The analysis provides insight in that the reprogrammability asymmetry appears to originate from a non-monotonic relationship to algorithmic probability, which motivates further analysis of the origin and consequences of the aforementioned asymmetries, reprogmability, and computation.

### Algorithmic information and incompressibility of families of multidimensional networks.

- Computer Science
- 2018

This article presents a theoretical investigation of string-based generalized representations of families of finite networks in a multidimensional space that are formalized in the form of multiaspect graphs and shows that, unlike classical graphs, the algorithmic information of a multi-dimensional network is not in general dominated by the algorithm of the binary sequence that determines the presence or absence of edges.

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