iDEC: Indexable distance estimating codes for approximate nearest neighbor search

Long Gong, Huayi Wang, Mitsunori Ogihara, Jun Xu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Approximate Nearest Neighbor (ANN) search is a fundamental algorithmic problem, with numerous applications in many areas of computer science. In this work, we propose indexable distance estimating codes (iDEC), a new solution framework to ANN that extends and improves the locality sensitive hashing (LSH) framework in a fundamental and systematic way. Empirically, an iDEC-based solution has a low index space complexity of O(n) and can achieve a lowaverage query time complexity of approximately O(log n). We show that our iDEC-based solutions for ANN in Hamming and edit distances outperform the respective state-of-theart LSH-based solutions for both in-memory and externalmemory processing. We also show that our iDEC-based in-memory ANN-H solution is more scalable than all existing solutions. We also discover deep connections between Error-Estimating Codes (EEC), LSH, and iDEC.

Original languageEnglish (US)
Pages (from-to)1483-1497
Number of pages15
JournalProceedings of the VLDB Endowment
Issue number9
StatePublished - May 1 2020

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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