Supervised Learning in Parallel Universes using Neighborgrams

We present a supervised method for Learning in Parallel Universes, i.e. problems given in multiple descriptor spaces. The goal is the construction of local models in individual universes and their fusion to a superior global model that comprises all the available information from the given universes. We employ a predictive clustering approach using Neighborgrams, a one-dimensional data structure for the neighborhood of a single object in a universe. We also present an intuitive visualization, which allows for interactive model construction and visual comparison of cluster neighborhoods across universes