title: A self-stabilizing learning rule for minor component analysis.
abstract: The paper reviews single-neuron learning rules for minor component analysis and suggests a novel minor component learning rule. In this rule, the weight vector length is self-stabilizing, i.e., moving towards unit length in each learning step. In simulations with low- and medium-dimensional data, the performance of the novel learning rule is compared with previously suggested rules.
authors.affiliation: Computer Engineering Group, Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany. moeller@techfak.uni-bielefeld.de
authors.forename: Ralf
authors.initials: R
authors.lastname: Möller
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D019992:Databases as Topic; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D016571:Neural Networks, Computer