Optimal Unsupervised Learning in Feedforward Neural Networks
Author(s)
Sanger, Terence D.
DownloadAITR-1086.ps (8.262Mb)
Additional downloads
Metadata
Show full item recordAbstract
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. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.
Date issued
1989-01-01Other identifiers
AITR-1086
Series/Report no.
AITR-1086