A self-stabilizing learning rule for minor component analysis
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1Computer Engineering Group, Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany. moeller@techfak.uni-bielefeld.de
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.
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