PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

PSOM+ : Parametrized Self-Organizing Maps for noisy and incomplete data
Stefan Klanke and Helge Ritter
In: Workshop on Self-Organizing Maps (WSOM) 2005, September 2006, Paris, France.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3430
Deposited By:Stefan Klanke
Deposited On:10 February 2008