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Found 4 papers, 2 papers with code
Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification
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.
Data Structures and Algorithms Information Theory Information Theory Physics and Society
Identification of Seed Cells in Multispectral Images for GrowCut Segmentation
These homogenous zones are representatives of different types of land coverings in the image and are used as seeds for the GrowCut multispectral segmentation algorithm.
A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity
We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low.
Information Theory Computational Complexity Information Theory H.1.1
Unsupervised Segmentation of Multispectral Images with Cellular Automata
Multispectral images acquired by satellites are used to study phenomena on the Earth's surface.
