The algorithm of noisy k-means

Camille Brunet; Sébastien Loustau

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arXiv · 1308.3314

The algorithm of noisy k-means

Camille Brunet·Sébastien Loustau

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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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BibTeX RIS

Camille Brunet, Sébastien Loustau. 2013-08-15. The algorithm of noisy k-means. https://arxiv.org/abs/1308.3314

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