Abstract
We introduce a method to derive \(\varepsilon \)-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 \(\varepsilon \)-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.
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Funding
This work has been supported by the “RNAct” Marie Sklodowska-Curie Action (MSCA) Innovative Training Networks (ITN) H2020-MSCA-ITN-2018 (contract n\(^\circ \) 813239).
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Moniot, A., Chauvot de Beauchêne, I., Guermeur, Y. (2022). Inferring \(\varepsilon \)-nets of Finite Sets in a RKHS. In: Faigl, J., Olteanu, M., Drchal, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM+ 2022. Lecture Notes in Networks and Systems, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-15444-7_6
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DOI: https://doi.org/10.1007/978-3-031-15444-7_6
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