Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

February 15, 1996, Vol. 8, No. 2, Pages 256-259
(doi: 10.1162/neco.1996.8.2.256)
© 1996 Massachusetts Institute of Technology
Associative Memory with Uncorrelated Inputs
Article PDF (177.46 KB)
Abstract

In hybrid learning schemes a layer of unsupervised learning is followed by supervised learning. In this situation a connection between two unsupervised learning algorithms, principal component analysis and decorrelation, and a supervised learning algorithm, associative memory, is shown. When associative memory is preceded by principal component analysis or decorrelation it is possible to take advantage of the lack of correlation among inputs to associative memory to show that correlation matrix memory is a least squares solution to the supervised learning problem.