PUB - Publikationen an der Universität Bielefeld

This Matlab (R) toolbox provides several algorithms to learn a linear mapping from an n-dimensional source space to an m-dimensional target space, such that it makes a classification or clustering model that has been trained in the source space applicable in the target space. The source space model is assumed to be either a vector quantization model (such as learning vector quantizations and variations thereof, neural gas or k-Means) or a (labelled) mixture of Gaussians. The target space may be any vector space, but this toolbox will typically fail if the relationship between source and target space is highly nonlinear. In contrast, this toolbox is particularly effective if the difference between source and target space can be expressed in terms of simple, linear transformations such as rotations and scalings.