2013/08/15 by Camille Brunet, Brunet, Camille, Sébastien Loustau +1
Computer Science · Engineering · Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1308.3314
openalex publication_date 2013/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.