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Conference Papers Year : 2022

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

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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Dates and versions

hal-03823878 , version 1 (21-10-2022)

Identifiers

  • HAL Id : hal-03823878 , version 1

Cite

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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