Efficient computation of distribution kernels and distances

Research output: Contribution to book or proceedingConference articlepeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015
EditorsHamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti, George Jandieri, Gerald Schaefer, Ashu M. G. Solo
PublisherCSREA Press
Pages238-241
Number of pages4
ISBN (Electronic)1601324049, 9781601324047
StatePublished - 2015
Event2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015, at WORLDCOMP 2015 - Las Vegas, United States
Duration: Jul 27 2015Jul 30 2015

Publication series

NameProceedings of the 2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015

Conference

Conference2015 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2015, at WORLDCOMP 2015
Country/TerritoryUnited States
CityLas Vegas
Period07/27/1507/30/15

Keywords

  • Algorithm
  • Density
  • Distance
  • Distribution
  • Kernel

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