Differential priors for elastic nets

Miguel Á Carreira-Perpiñán, Peter Dayan, Geoffrey J. Goodhill

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)335-342
Number of pages8
JournalLecture Notes in Computer Science
Volume3578
DOIs
StatePublished - 2005
Event6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005 - Brisbane, Australia
Duration: Jul 6 2005Jul 8 2005

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