Learning in parallel universes

We discuss Learning in parallel universes as a learning concept that encompasses the simultaneous analysis from multiple descriptor spaces. In contrast to existing approaches, this approach constructs a global model that is based on only partially applicable, local models in each descriptor space. We present some application scenarios and compare this learning strategy to other approaches on learning in multiple descriptor spaces. As a representative for learning in parallel universes we introduce different extensions to a family of unsupervised fuzzy clustering algorithms and evaluate their performance on an artificial data set and a benchmark of 3D objects