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Frontiers of Computer Science

, Volume 14, Issue 2, pp 259–272 | Cite as

Optimized high order product quantization for approximate nearest neighbors search

  • Linhao LiEmail author
  • Qinghua Hu
Research Article

Abstract

Product quantization is now considered as an effective approach to solve the approximate nearest neighbor (ANN) search. A collection of derivative algorithms have been developed. However, the current techniques ignore the intrinsic high order structures of data, which usually contain helpful information for improving the computational precision. In this paper, aiming at the complex structure of high order data, we design an optimized technique, called optimized high order product quantization (O-HOPQ) for ANN search. In O-HOPQ, we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way. As a result, spatial adjacent elements in the high order data space are grouped into the same sub-space. Then, O-HOPQ generates its spatial structured code-book, by optimizing the quantization distortion. Starting from the structured codebook, the global optimum quantizers can be obtained effectively and efficiently. Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers. Besides, the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.

Keywords

product quantization high order structured data tensor theory approximate nearest neighbor search 

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Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61732011) and Applied Fundamental Research Program of Qinghai Province (2019-ZJ-7017).

Supplementary material

11704_2018_7049_MOESM1_ESM.pdf (286 kb)
Optimized high order product quantization for approximate nearest neighbors search

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Artificial IntelligenceHebei University of TechnologyTianjinChina
  2. 2.Tianjin Key Lab of Machine Learning, School of Computer Science and TechnologyTianjin UniversityTianjinChina

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