title: A robust information clustering algorithm.
abstract: We focus on the scenario of robust information clustering (RIC) based on the minimax optimization of mutual information (MI). The minimization of MI leads to the standard mass-constrained deterministic annealing clustering, which is an empirical risk-minimization algorithm. The maximization of MI works out an upper bound of the empirical risk via the identification of outliers (noisy data points). Furthermore, we estimate the real risk VC-bound and determine an optimal cluster number of the RIC based on the structural risk-minimization principle. One of the main advantages of the minimax optimization of MI is that it is a nonparametric approach, which identifies the outliers through the robust density estimate and forms a simple data clustering algorithm based on the square error of the Euclidean distance.
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. eqsong@ntu.edu.sg
authors.forename: Qing
authors.initials: Q
authors.lastname: Song
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical; D009716:Numerical Analysis, Computer-Assisted