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

Hervé Frezza-Buet (1, 2)

1 CentraleSupélec
2 BISCUIT - Bio-Inspired, Situated and Cellular Unconventional Information Technologies

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.

Domaines: Réseau de neurones [cs.NE]

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⟩