| Paper: | IMDSP-P8.11 (ICASSP 2003 Paper) | ||
| Session: | Image and Multidimensional Signal Processing: Applications I | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster (ICASSP 2003 Presentation) | ||
| Topic: | Image and Multidimensional Signal Processing: M-D Signal Processing Theory and Methods | ||
| Title: | A NEW CLASS OF ENTROPY ESTIMATORS FOR MULTI-DIMENSIONAL DENSITIES | ||
| Authors: | Erik Miller; University of California, Berkeley | ||
| Abstract: | We present a new class of estimators for approximating the entropy of multi-dimensional probability densities based on a sample of the density. These estimators extend the classic ''m-spacing'' estimators of Vasicek and others for estimating entropies of one-dimensional probability densities. Unlike plug-in estimators of entropy, which first estimate a probability density and then compute its entropy, our estimators avoid the difficult intermediate step of density estimation. For fixed dimension, the estimators are polynomial in the sample size. Similarities to consistent and asymptotically efficient one-dimensional estimators of entropy suggest that our estimators may share these properties. | ||
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