Clustering and causality inference using algorithmic complexity. Revolle, M., Cayre, F., & Le Bihan, N. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 843-847, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex We present a set of algorithmic complexity estimates. We derive a normalized semi-distance that is shown to outperform the state-of-the-art. We also propose estimators for causality inference on directed acyclic graphs. Illustrative applications include clustering of human writing systems and causality assessment on novel drafts.
@InProceedings{8081326,
author = {M. Revolle and F. Cayre and N. {Le Bihan}},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Clustering and causality inference using algorithmic complexity},
year = {2017},
pages = {843-847},
abstract = {We present a set of algorithmic complexity estimates. We derive a normalized semi-distance that is shown to outperform the state-of-the-art. We also propose estimators for causality inference on directed acyclic graphs. Illustrative applications include clustering of human writing systems and causality assessment on novel drafts.},
keywords = {computational complexity;directed graphs;graph theory;pattern clustering;causality inference;algorithmic complexity estimates;estimators;directed acyclic graphs;causality assessment;clustering;human writing systems;Complexity theory;Signal processing algorithms;Inference algorithms;Encoding;Europe;Signal processing},
doi = {10.23919/EUSIPCO.2017.8081326},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347023.pdf},
}Downloads: 0
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