Distributed High Dimensional Information Theoretical Image Registration via Random Projections

However, the estimation of these quantities is computationally intensive in high dimensions. On the other hand, consistent estimation from pairwise distances of the sample points is possible, which suits random projection(RP) based low dimensional embeddings. We adapt the RP technique to this task by means of a simple ensemble method. To the best of our knowledge, this is the first distributed, RP based information theoretical image registration approach. The efficiency of the method is demonstrated through numerical examples. Keywords: random projection, information theoretical image registration, high dimensional features, distributed solution 1. Introduction Machine learning methods are notoriously limited by the high dimensional nature of the data. This problem may be alleviated via the random projection (RP) technique, which has been successfully applied, e.g., in the fields of