k-Nearest Neighbor Based Consistent Entropy Estimation for Hyperspherical Distributions

Entropy

关键词: hyperspherical distribution;    directional data;    differential entropy;    cross entropy;    Kullback-Leibler divergence;    k-nearest neighbor;   

DOI  :  10.3390/e13030650

来源: DOAJ

【 摘 要 】

A consistent entropy estimator for hyperspherical data is proposed based on the k-nearest neighbor (knn) approach. The asymptotic unbiasedness and consistency of the estimator are proved. Moreover, cross entropy and Kullback-Leibler (KL) divergence estimators are also discussed. Simulation studies are conducted to assess the performance of the estimators for models including uniform and von Mises-Fisher distributions. The proposed knn entropy estimator is compared with the moment based counterpart via simulations. The results show that these two methods are comparable.

【 授权许可】

Unknown