The algorithm of noisy k-means
This is an incremental method for clustering in noisy data scenarios, with limited novelty and unclear impact.
The paper tackles clustering with noisy inputs by introducing a new algorithm that combines deconvolution to handle errors in variables and Newton's iterations from k-means, but no concrete results or numbers are provided.
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.