Pivot selection for metric-space indexing

  • Rui Mao
  • Peihan Zhang
  • Xingliang Li
  • Xi Liu
  • Minhua LuEmail author
Original Article


Metric-space indexing abstracts various data types into universal metric spaces and prunes data only exploiting the triangle inequality of the distance function in metric spaces. Since there is no coordinates in metric space, one usually first pick a number of reference points, pivots, and consider the distances from a data point to the pivots as its coordinates. In this paper, we first survey and discuss the state of the art of pivot selection for metric-space indexing from the perspectives of importance, objective function, number of pivots, and selection algorithm. Further, we propose a new objective function, a new method to determine the number of pivots and an incremental sampling framework for pivot selection. Experimental results show that the new objective function is more consistent with the query performance, the new method to determine the number of pivots is more efficient, and the incremental sampling framework leads to better query performance.


Metric-space indexing Pivot selection Intrinsic dimension Objective function Range query 



This work is partially supported by the following Grants: China 863: 2015AA015305; NSF-China: 61170076, U1301252, 61471243; Guangdong Key Laboratory Project: 2012A061400024; NSF-Shenzhen: JCYJ20140418095735561, JCYJ20150731160834611; Shenzhen-Hong Kong Innovation circle Project: SGLH20131010163759789. Dr. Minhua Lu is the corresponding author.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Rui Mao
    • 1
  • Peihan Zhang
    • 1
  • Xingliang Li
    • 2
  • Xi Liu
    • 1
  • Minhua Lu
    • 3
    Email author
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.College of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  3. 3.School of MedicineShenzhen UniversityShenzhenChina

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