- AutorIn
- Ralf Der
- Ulrich Steinmetz
- Gerd Balzuweit
- Gerrit Schüürmann
- Titel
- Nonlinear principal component analysis
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-345209
- Erschienen in
- Report / Institut für Informatik
- Bandnummer
- 1998,4
- Erstveröffentlichung
- 1998
- Abstract (EN)
- We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organizing maps. We present a general algorithm which maps low-dimensional lattices into high-dimensional data manifolds without violation of topology. 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. Moreover we present a second algorithm for the extraction of generalized principal curves comprising disconnected and branching manifolds. The performance of the algorithm is demonstrated for both one- and two-dimensional principal manifolds and also for the case of sparse data sets. As an application we reveal cluster structures in a set of real world data from the domain of ecotoxicology.
- Freie Schlagwörter (EN)
- nonlinear data models, ecotoxicology
- Klassifikation (DDC)
- 004
- Publizierende Institution
- Universität Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-345209
- Veröffentlichungsdatum Qucosa
- 15.07.2019
- Dokumenttyp
- Buch
- Sprache des Dokumentes
- Englisch