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The algorithm of noisy k-means

Abstract : 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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Contributor : Sébastien Loustau Connect in order to contact the contributor
Submitted on : Wednesday, August 14, 2013 - 2:08:09 PM
Last modification on : Wednesday, October 20, 2021 - 3:18:47 AM
Long-term archiving on: : Wednesday, April 5, 2017 - 8:46:19 PM


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  • HAL Id : hal-00851484, version 1
  • ARXIV : 1308.3314



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



Les métriques sont temporairement indisponibles