Optimal Unsupervised Learning in Feedforward Neural Networks

Abstract

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. If a network trained in this way is used as input to a layer trained using the Widrow-Hoff (LMS) algorithm, the system implements an optimal Wiener filter. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. The algorithm to texture processing, image coding, and stereo depth edge detection are given. It is shown that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA207961

Entities

People

  • Terence D. Sanger

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cognitive Science
  • Computer Programming
  • Computer Science
  • Computer Vision
  • Data Science
  • Differential Equations
  • Electrical Engineering
  • Factor Analysis
  • Image Processing
  • Information Processing
  • Information Science
  • Information Systems
  • Network Science
  • Neural Networks
  • Optimal Estimators
  • Signal Processing

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Phased Array Antenna Design.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks