# 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 Sanger T. D., *Single-Layer Linear Feedforward Neural Network*, 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 in descending order . The resulting approach, which is a natural extension of Oja's rule, has been called the **Generalized Hebbian Rule** (**GHA**)—you might also sometimes see it called **Generalized Hebbian Learning** (**GHL**). The structure of the network is represented in the following diagram:

Structure of a Sanger's Network

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* = {*w*_{ij}}, where the first index refers to the input components (pre-synaptic units) and...