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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:32410 |
| Date | 10 December 2018 |
| Creators | Der, Ralf, Balzuweit, Gerd, Herrmann, Michael |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-540-61510-1 |
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