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Conference Papers Year : 2022

Inferring Epsilon-nets of Finite Sets in a RKHS


We introduce a method to derive epsilon-nets of finite sets. It operates in a reproducing kernel Hilbert space. Its principle combines two well-known tools of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces epsilon-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.
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hal-03651323 , version 1 (25-04-2022)



Antoine Moniot, Isaure Chauvot de Beauchêne, Yann Guermeur. Inferring Epsilon-nets of Finite Sets in a RKHS. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM - 2022, Jul 2022, Prague, Czech Republic. pp.53-62, ⟨10.1007/978-3-031-15444-7_6⟩. ⟨hal-03651323⟩
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