Mastering Machine Learning Algorithms

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, ..., v_m) in descending order (λ₁ > λ₂ > ... > λ_m). 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 ...