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An eigenvalue-based pivot selection strategy for efficient indexing and searching in metric spaces

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Abstract

Pivots are used widely during indexing and searching in metric spaces. We maintain the distances from pivots to data objects to be indexed so the pre-computed distances can be used to prune unpromising objects during the search process. The search efficiency depends on the pivots used, but choosing good pivots is a challenging task. In this paper, we propose a new pivot selection method that incrementally chooses pivots using an eigenvalue-based uncorrelatedness scoring function. We also present a GPU implementation for computing the uncorrelatedness score in order to accelerate the pivot selection process. Our experimental results demonstrated that the proposed method performed better than other previously described pivot selection methods.

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Acknowledgements

This research was supported by Basic Research Laboratory through the National Research Foundations of Korea funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1A4A1041584). A preliminary version of this paper appeared in [11].

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  1. Department of Electrical and Computer Engineering, Pusan National University, Busan, South Korea

    Sung-Hwan Kim, Da-Young Lee & Hwan-Gue Cho

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  1. Sung-Hwan Kim
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  2. Da-Young Lee
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  3. Hwan-Gue Cho
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Correspondence to Hwan-Gue Cho.

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Kim, SH., Lee, DY. & Cho, HG. An eigenvalue-based pivot selection strategy for efficient indexing and searching in metric spaces. Cluster Comput 20, 3643–3655 (2017). https://doi.org/10.1007/s10586-017-1153-4

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