Building nonlinear data models with self-organizing feature maps

Authors: 
Der, Ralf
Balzuweit, Gerd
Herrmann, Michael
Year: 
1996
Language: 
English
Abstract: 
We study the extraction of nonlinear data models in high dimensional spaces with modified self-organizing maps. Our algorithm maps lower dimensional lattice into a high dimensional space without topology violations by tuning the neighborhood widths locally. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. The performance of the algorithm is demonstrated for one- and two-dimensional principal manifolds and for sparse data sets.
Appeared / Erschienen in: 
Lecture Notes in Computer Science 1112: Artificial Neural Networks - ICANN 96, S. 821 - 826. Springer 1996
Pubdate / Erscheinungsdatum: 
1996
Pages / Seitenanzahl: 
6
AttachmentSize
1996-33.pdf405.03 KB