Sanger's network
A Sanger's network is a neural network model for online Principal Component extraction proposed by T. D. Sanger in Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network, Sanger T. D., Neural Networks, 1989/2. The author started with the standard version of Hebb's rule and modified it to be able to extract a variable number of principal components (v1, v2, ..., vm) in descending order (λ1 > λ2 > ... > λm). The resulting approach, which is a natural extension of Oja's rule, has been called the Generalized Hebbian Rule (GHA) (or Learning). The structure of the network is represented in the following diagram:
The network is fed with samples extracted from an n-dimensional dataset:
The m output neurons are connected to the input through a weight matrix, W = {wij}, where the first index refers to the input components (pre-synaptic units) and the second one to the neuron. The output of the network can be easily computed with a scalar product;...
