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18th GAMM-Seminar Leipzig on
Multigrid and related methods for optimization problems

Max-Planck-Institute for Mathematics in the Sciences
Inselstr. 22-26, D-04103 [O->]Leipzig
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  18th GAMM-Seminar
January, 24th-26th, 2002
 
     
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  Abstract Jochen Garcke, Thu, 16.30-16.55 Previous Contents Next  
  Classification with sparse grids
Jochen Garcke (Uni Bonn)

We present a new approach to the classification problem arising in data mining. It is based on the regularization network approach and uses basis functions coming from a grid in the usually high-dimensional feature space for the minimization process. To cope with the curse of dimensionality, we employ sparse grids. To be precise, we suggest to use the sparse grid combination technique where the classification problem is discretized and solved on a certain sequence of conventional grids with uniform mesh sizes in each coordinate direction. The sparse grid solution is then obtained from the solutions on these different grids by linear combination.

The method computes a nonlinear classifier but scales only linearly with the number of instances, i.e. the amount of data to be classified. It is therefore well suited for data mining applications where the amount of data is very large, but where the dimension of the feature space is moderately high.
 

 
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Last updated:
30.11.2004 Impressum
 
Concept, Design and Realisation
[O->]Jens Burmeister (Uni Kiel), Kai Helms (MPI Leipzig)
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