Differential Priors for Elastic Nets

Carreira-Perpinan, M., Dayan, P. and Goodhill, G. J. (2005). Differential Priors for Elastic Nets. In: M. Gallagher, J. Hogan and F. Maire, Lecture Notes in Computer Science: Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005, Brisbane Australia, (335-342). July 6-8. doi:10.1007/11508069_44


Author Carreira-Perpinan, M.
Dayan, P.
Goodhill, G. J.
Title of paper Differential Priors for Elastic Nets
Conference name IDEAL 2005
Conference location Brisbane Australia
Conference dates July 6-8
Proceedings title Lecture Notes in Computer Science: Intelligent Data Engineering and Automated Learning - IDEAL 2005   Check publisher's open access policy
Journal name Intelligent Data Engineering and Automated Learning Ideal 2005, Proceedings   Check publisher's open access policy
Place of Publication Berlin
Publisher Springer
Publication Year 2005
Sub-type Fully published paper
DOI 10.1007/11508069_44
ISBN 3-540-26972-X
ISSN 0302-9743
1611-3349
Editor M. Gallagher
J. Hogan
F. Maire
Volume 3578
Start page 335
End page 342
Total pages 8
Language eng
Abstract/Summary The elastic net and related algorithms, such as generative topographic mapping, are key methods for discretized dimension-reduction problems. At their heart are priors that specify the expected topological and geometric properties of the maps. However, up to now, only a very small subset of possible priors has been considered. Here we study a much more general family originating from discrete, high-order derivative operators. We show theoretically that the form of the discrete approximation to the derivative used has a crucial influence on the resulting map. Using a new and more powerful iterative elastic net algorithm, we confirm these results empirically, and illustrate how different priors affect the form of simulated ocular dominance columns.
Subjects E1
239901 Biological Mathematics
780101 Mathematical sciences
Q-Index Code E1

 
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Created: Fri, 24 Aug 2007, 07:03:09 EST