Differential Priors for Elastic Nets

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Differential Priors for Elastic Nets
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.
Miguel Á. Carreira-Perpiñán,
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Authors Miguel Á. Carreira-Perpiñán, Peter Dayan, Geoffrey J. Goodhill
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