We address the problem of entropy estimation for high-dimensional finite-accuracy data. Our main application is evaluating high-order mutual information image similarity criteria for multimodal image registration. The basis of our method is an estimator based on k-th nearest neighbor (NN) distances, modified so that only distances greater than some constant R are evaluated. This modification requires a correction which is found numerically in a preprocessing step using quadratic programming. We compare experimentally our new method with k-NN and histogram estimators on synthetic data as well as for evaluation of mutual information for image similarity. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kybic, J. (2007). High-dimensional entropy estimation for finite accuracy data: R-NN entropy estimator. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4584 LNCS, pp. 569–580). Springer Verlag. https://doi.org/10.1007/978-3-540-73273-0_47
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