2013/01/04 by Yoshikazu Terada, Terada, Yoshikazu
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #FOS: Mathematics #Face and Expression Recognition #Statistics Theory (math.ST) #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1301.0676
A revised version of this was accepted in Annals of the Institute of Statistical Mathematics. Please refer to the accepted version of this. In the accepted ver., I describe a new interesting fact that there exists some cases in which reduced k-means clustering becomes equivalent to FKM clustering as n goes to infinity and provide a rough large deviation inequality for FKM clustering
openalex publication_date 2013/01/04 · arxiv created 2014/02/13 · arxiv updated 2014/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Factorial k-means (FKM) clustering is a method for clustering objects in a low-dimensional subspace. The advantage of this method is that the partition of objects and the low-dimensional subspace reflecting the cluster structure are obtained, simultaneously. Conditions that ensure the almost sure convergence of the estimator of FKM clustering as the sample size increases unboundedly are derived. The result is proved for a more general model including FKM clustering.