Efficient anchor graph hashing with data-dependent anchor selection

Hiroaki Takebe, Yusuke Uehara, Seiichi Uchida

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space.

Original languageEnglish
Pages (from-to)2030-2033
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE98D
Issue number11
DOIs
Publication statusPublished - Nov 2015

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Anchors
Principal component analysis

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

Efficient anchor graph hashing with data-dependent anchor selection. / Takebe, Hiroaki; Uehara, Yusuke; Uchida, Seiichi.

In: IEICE Transactions on Information and Systems, Vol. E98D, No. 11, 11.2015, p. 2030-2033.

Research output: Contribution to journalArticle

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