@inproceedings{bf530d2aa2a646348a9683a72c50d6af,
title = "Efficient computation of distribution kernels and distances",
abstract = "Similarity and dissimilarity measures such as kernels and distances are key components of classification and clustering algorithms. We propose an efficient algorithm for computation of kernel and distance functions between two probability distributions. The complexity of the proposed algorithm is insensitive to the dimension of the input space and therefore especially suitable for high dimensional distributions.",
keywords = "Algorithm, Density, Distance, Distribution, Kernel",
author = "Hongjun Su and Hong Zhang",
note = "Publisher Copyright: {\textcopyright} International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015.All right reserved.; 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015, at WORLDCOMP 2015 ; Conference date: 27-07-2015 Through 30-07-2015",
year = "2015",
language = "English",
series = "Proceedings of the 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015",
publisher = "CSREA Press",
pages = "238--241",
editor = "Arabnia, {Hamid R.} and Leonidas Deligiannidis and Tinetti, {Fernando G.} and George Jandieri and Gerald Schaefer and Solo, {Ashu M. G.}",
booktitle = "Proceedings of the 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015",
}