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Pré-Publication, Document De Travail Année : 2013

The algorithm of noisy k-means

Résumé

In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances (see Loustau (2013a,b)) in statistical learning with errors in variables. As the previous mentioned papers, the algorithm mixes different tools from the inverse problem literature and the machine learning community. Coarsely, it is based on a two-step procedure: (1) a deconvolution step to deal with noisy inputs and (2) Newton's iterations as the popular k-means.
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Dates et versions

hal-00851484 , version 1 (14-08-2013)

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Camille Brunet, Sébastien Loustau. The algorithm of noisy k-means. 2013. ⟨hal-00851484⟩
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