An Efficient Exact Nearest Neighbor Search by Compounded Embedding

  • Mingjie LiEmail author
  • Ying Zhang
  • Yifang Sun
  • Wei Wang
  • Ivor W. Tsang
  • Xuemin Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


Nearest neighbor search (NNS) in high dimensional space is a fundamental and essential operation in applications from many domains, such as machine learning, databases, multimedia and computer vision. In this paper, we first propose a novel and effective distance lower bound computation technique for Euclidean distance by using the combination of linear and non-linear embedding methods. As such, each point in a high dimensional space can be embedded into a low dimensional space such that the distance between two embedded points lower bounds their distance in the original space. Following the filter-and-verify paradigm, we develop an efficient exact NNS algorithm by pruning candidates using the new lower bounding technique and hence reducing the cost of expensive distance computation in high dimensional space. Our comprehensive experiments on 10 real-life and diverse datasets, including image, video, audio and text data, demonstrate that our new algorithm can significantly outperform the state-of-the-art exact NNS techniques.



Ying Zhang is supported by ARC DE140100679 and DP170103710. Wei Wang is supported by ARC DP170103710, and D2DCRC DC25002 and DC25003. Ivor W. Tsang is supported by ARC grant FT130100746, DP180100106, and LP150100671. Xuemin Lin is supported by NSFC 61672235, DP170101628 and DP180103096.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mingjie Li
    • 1
    Email author
  • Ying Zhang
    • 1
  • Yifang Sun
    • 2
  • Wei Wang
    • 2
  • Ivor W. Tsang
    • 1
  • Xuemin Lin
    • 2
  1. 1.Centre for Artificial IntelligenceUniversity of Technology SydneySydneyAustralia
  2. 2.The University of New South WalesSydneyAustralia

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