Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27707
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Connor, Richard
Cardillo, Franco Alberto
Moss, Robert
Rabitti, Fausto
Contact Email: richard.connor@stir.ac.uk
Title: Evaluation of Jensen-Shannon distance over sparse data
Editor(s): Brisaboa, N
Pedreira, O
Zezula, P
Citation: Connor R, Cardillo FA, Moss R & Rabitti F (2013) Evaluation of Jensen-Shannon distance over sparse data. In: Brisaboa N, Pedreira O & Zezula P (eds.) Similarity Search and Applications: 6th International Conference, SISAP 2013, A Coruña, Spain, October 2-4, 2013, Proceedings. Lecture Notes in Computer Science, 8199. Similarity Search and Applications: 6th International Conference, SISAP 2013, Coruna, Spain, 02.10.2013-04.10.2013. Berlin, Heidelberg: Springer Verlag, pp. 163-168. https://doi.org/10.1007/978-3-642-41062-8_16
Issue Date: 31-Dec-2013
Series/Report no.: Lecture Notes in Computer Science, 8199
Conference Name: Similarity Search and Applications: 6th International Conference, SISAP 2013
Conference Dates: 2013-10-02 - 2013-10-04
Conference Location: Coruna, Spain
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.
Status: VoR - Version of Record
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