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Graph-Induced Geodesics Approximation for Non-Euclidian K-Means

Hervé Frezza-Buet 1, 2 
2 BISCUIT - Bio-Inspired, Situated and Cellular Unconventional Information Technologies
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : In this paper, an adaptation of the k-means algorithm and related methods to non-Euclidian topology is presented. The paper introduces a rationale for approximating the geodesics of that topology, as well as a learning rule that is robust to noise. The first results on artificial but very noisy distributions presented here are promising for further experimentation on real cases.
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Submitted on : Friday, October 21, 2022 - 11:33:42 AM
Last modification on : Sunday, November 6, 2022 - 2:39:18 PM


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  • HAL Id : hal-03823878, version 1


Hervé Frezza-Buet. Graph-Induced Geodesics Approximation for Non-Euclidian K-Means. ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2022, Bruges, Belgium. ⟨hal-03823878⟩



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