The algorithm of noisy kmeans
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 twostep procedure: (1) a deconvolution step to deal with noisy inputs and (2) Newton's iterations as the popular kmeans.
 Publication:

arXiv eprints
 Pub Date:
 August 2013
 DOI:
 10.48550/arXiv.1308.3314
 arXiv:
 arXiv:1308.3314
 Bibcode:
 2013arXiv1308.3314B
 Keywords:

 Statistics  Machine Learning;
 Computer Science  Machine Learning