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GeoKernels: modeling of spatial data on geomanifolds

Alexei Pozdnoukhov, Mikhail F. Kanevski
2008 The European Symposium on Artificial Neural Networks  
It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief.  ...  The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.  ...  Learning on GeoManifolds The environmental processes captured by modern monitoring networks can not be generally explained in 2D spatial coordinates.  ... 
dblp:conf/esann/PozdnoukhovK08 fatcat:35h6xwrnavgrviuvtrl4tq6i6m

Generative Manifold Learning for the Exploration of Partially Labeled Data

Raúl Cruz-Barbosa, Alfredo Vellido
2013 Journal of Computacion y Sistemas  
A variant of GTM that uses a graph approximation to the geodesic metric is first defined. This model is capable of representing data of convoluted geometries.  ...  In SS-Geo-GTM, the model prototypes obtained from Geo-GTM are linked by the nearest neighbour to the data manifold.  ...  Cruz-Barbosa acknowledges the Mexican Secretariat of Public Education (SEP-PROMEP program) for his PhD grant.  ... 
doi:10.13053/cys-17-4-2013-014 fatcat:bxvoxaczqnan7mop6wx7sojqwy