DocumentCode
2940818
Title
A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors
Author
Wang, Qing ; Kulkarni, Sanjeev R. ; Verdu, Sergio
Author_Institution
Dept. of Electr. Eng., Princeton Univ., NJ
fYear
2006
fDate
9-14 July 2006
Firstpage
242
Lastpage
246
Abstract
A method for divergence estimation between multidimensional distributions based on nearest neighbor distances is proposed. Given i.i.d. samples, both the bias and the variance of this estimator are proven to vanish as sample sizes go to infinity. In experiments on high-dimensional data, the nearest neighbor approach generally exhibits faster convergence compared to previous algorithms based on partitioning
Keywords
statistical distributions; continuous random vectors; divergence estimation; multidimensional distributions; nearest-neighbor distances approach; Convergence; Entropy; Frequency estimation; H infinity control; Multidimensional systems; Nearest neighbor searches; Neural networks; Partitioning algorithms; Probability distribution; Random variables;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory, 2006 IEEE International Symposium on
Conference_Location
Seattle, WA
Print_ISBN
1-4244-0505-X
Electronic_ISBN
1-4244-0504-1
Type
conf
DOI
10.1109/ISIT.2006.261842
Filename
4035959
Link To Document