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PSOM +: Parametrized Self-Organizing Maps for noisy and incomplete data PSOM +: Parametrized Self-Organizing Maps for noisy and incomplete data (2008)

Abstract
Abstract- We present an extension to the Parametrized Self-Organizing Map that allows the construction of continuous manifolds from noisy, incomplete and not necessarily gridorganized training data. All three problems are tackled by minimizing the overall smoothness of a PSOM manifold. For this, we introduce a matrix which defines a metric in the space of PSOM weights, depending only on the underlying grid layout. We demonstrate the method with several examples, including the kinematics of a PA10 robot arm. Key words- interpolation, function approximation, regularization, missing data 1

Publication details
Download http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.1295
Source http://www.techfak.uni-bielefeld.de/ags/ni/publications/media/KlankeRitter2005-PPS.pdf
Contributors CiteSeerX
Repository CiteSeerX - Scientific Literature Digital Library and Search Engine (United States)
Type text
Language English
Relation 10.1.1.44.8495, 10.1.1.32.9350, 10.1.1.44.6801