Abstract
Nowadays the nearest neighbor (NN) search in the high dimensional space can be applied in many fields and it becomes the focus of information science. Usually, R-near neighbor that sets a fixed query range R is used in place of NN search. However, the traditional methods for R-near neighbor can not achieve the satisfactory performance in the high dimensional space due to the curse of dimensionality. Moreover, some methods is based on probabilistic guarantees so it does not provide the 100 % accuracy guarantee. To improve the problem, in this paper, we propose a novel idea to build the index structure. This method is based on the mathematical features of the coordinates of the data points. Specifically, we employ the mean value and the standard deviation of the coordinate to index the data point. This method can efficiently solve the R-NN search with the 100 % accuracy guarantee in the high dimensional space. Extensive experimental results demonstrate the effectiveness of the proposed methods.
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Zhang, Y., Li, J., Yuan, Y. (2016). A Novel High-Dimensional Index Method Based on the Mathematical Features. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_22
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DOI: https://doi.org/10.1007/978-3-319-42553-5_22
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