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Self-organization by delta rule

In a certain two-layer network architecture, the delta learning rule leads to a self-organization of connection weights. A formal analysis of this learning rule shows that the weight vectors of n second-layer nodes converge to a rotation of the first n principal components. Therefore, the delta-rule-based self-organization performs optimal encoding and decoding of data in the sense of the principal component analysis. This property of the delta learning rule has been verified by a series of computational experiments, which also showed good convergence stability of the rule. For data compression tasks, it performs substantially better than a three-layer autoassociative perceptron with linear or nonlinear hidden units