Evaluation of Jensen-Shannon distance over sparse data
Connor, Richard and Cardillo, Franco Alberto and Moss, Robert and Rabitti, Fausto; Brisaboa, Nieves and Pedreira, Oscar and Zezula, Pavel, eds. (2013) Evaluation of Jensen-Shannon distance over sparse data. In: Similarity Search and Applications. Lecture Notes in Computer Science, 8199 . Springer, ESP, pp. 163-168. ISBN 9783642410611
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Abstract
Jensen-Shannon divergence is a symmetrised, smoothed version of Küllback-Leibler. It has been shown to be the square of a proper distance metric, and has other properties which make it an excellent choice for many high-dimensional spaces in R*. The metric as defined is however expensive to evaluate. In sparse spaces over many dimensions the Intrinsic Dimensionality of the metric space is typically very high, making similarity-based indexing ineffectual. Exhaustive searching over large data collections may be infeasible. Using a property that allows the distance to be evaluated from only those dimensions which are non-zero in both arguments, and through the identification of a threshold function, we show that the cost of the function can be dramatically reduced.
Creators(s): |
Connor, Richard ![]() | Item type: | Book Section |
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ID code: | 52372 |
Keywords: | distance metrics, exhaustive searching, high dimensional spaces, instrinsic dimensionalitites, Jensen-Shannon divergence, metric spaces, other properties, threshold functions, artificial intelligence, computer science, Electronic computers. Computer science, Information Systems |
Subjects: | Science > Mathematics > Electronic computers. Computer science |
Department: | Faculty of Science > Computer and Information Sciences |
Depositing user: | Pure Administrator |
Date deposited: | 31 Mar 2015 07:23 |
Last modified: | 25 Jan 2021 04:13 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/52372 |
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