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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 |
| Date Deposited: | 16-Aug-2018 |
| 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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