Neural network modeling of data with gaps: method of principal curves, Carleman's formula, and other

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dc.creatorGorban, A. N.
dc.creatorRossiev, A. A.
dc.creatorWunsch II, D. C.
dc.date2003-05-21
dc.date.accessioned2026-07-07T02:51:28Z
dc.date.available2026-07-07T02:51:28Z
dc.descriptionA method of modeling data with gaps by a sequence of curves has been developed. The new method is a generalization of iterative construction of singular expansion of matrices with gaps. Under discussion are three versions of the method featuring clear physical interpretation: linear - modeling the data by a sequence of linear manifolds of small dimension; quasilinear - constructing "principal curves: (or "principal surfaces"), univalently projected on the linear principal components; essentially non-linear - based on constructing "principal curves": (principal strings and beams) employing the variation principle; the iteration implementation of this method is close to Kohonen self-organizing maps. The derived dependencies are extrapolated by Carleman's formulas. The method is interpreted as a construction of neural network conveyor designed to solve the following problems: to fill gaps in data; to repair data - to correct initial data values in such a way as to make the constructed models work best; to construct a calculator to fill gaps in the data line fed to the input.
dc.description28 pages, 7 figures,The talk was given at the USA-NIS Neurocomputing opportunities workshop, Washington DC, July 1999 (Associated with IJCNN'99)
dc.identifierhttps://arxiv.org/abs/cond-mat/0305508
dc.identifierhttp://arxiv.org/abs/cond-mat/0305508
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/21467
dc.subjectDisordered Systems and Neural Networks
dc.subjectNeural and Evolutionary Computing
dc.subjectData Analysis, Statistics and Probability
dc.titleNeural network modeling of data with gaps: method of principal curves, Carleman's formula, and other
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