|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
Cardillo, Franco Alberto
|Title:||Evaluation of Jensen-Shannon distance over sparse data|
|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|
|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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