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

Neural Computation

October 2006, Vol. 18, No. 10, Pages 2293-2319
(doi: 10.1162/neco.2006.18.10.2293)
© 2006 Massachusetts Institute of Technology
Images, Frames, and Connectionist Hierarchies
Article PDF (874.18 KB)
Abstract

The representation of hierarchically structured knowledge in systems using distributed patterns of activity is an abiding concern for the connectionist solution of cognitively rich problems. Here, we use statistical unsupervised learning to consider semantic aspects of structured knowledge representation. We meld unsupervised learning notions formulated for multilinear models with tensor product ideas for representing rich information. We apply the model to images of faces.