PSOM+ : Parametrized Self-Organizing Maps for noisy and incomplete data
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.