Towards learning in parallel universes

Most learning algorithms operate in a clearly defined feature space and assume that all relevant structure can be found in this one, single space. For many local learning methods, especially the ones working on distance metrics (e.g. clustering algorithms) this poses a serious limitation. We discuss an algorithm that directly finds a set of cluster centers based on an analysis of the distribution of patterns in the local neighborhood of each potential cluster center through the use of so-called Neighborgrams. This type of cluster construction makes it feasible to find clusters in several feature spaces in parallel, effectively finding the optimal feature space for each cluster independently. We demonstrate how the algorithm works on an artificial data set and show its usefulness using a well-known benchmark data set.