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Pré-Publication, Document De Travail Année : 2021

Kernel Spectral Clustering

Résumé

We investigate the question of studying spectral clustering in a Hilbert space where the set of points to cluster are drawn i.i.d. according to an unknown probability distribution whose support is a union of compact connected components. We modify the algorithm proposed by Ng, Jordan and Weiss in order to propose a new algorithm that automatically estimates the number of clusters and we characterize the convergence of this new algorithm in terms of convergence of Gram operators. We also give a hint of how this approach may lead to learn transformation-invariant representations in the context of image classification.

Dates et versions

hal-03196142 , version 1 (12-04-2021)

Identifiants

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Ilaria Giulini. Kernel Spectral Clustering. 2021. ⟨hal-03196142⟩
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