title: Noise-enhanced clustering and competitive learning algorithms.
abstract: Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.
authors.affiliation: Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA.
authors.forename: Osonde
authors.initials: O
authors.lastname: Osoba
authors.forename: Bart
authors.initials: B
authors.lastname: Kosko
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D013269:Stochastic Processes