Efficient anchor graph hashing with data-dependent anchor selection

Hiroaki Takebe; Yusuke Uehara; Seiichi Uchida

doi:10.1587/transinf.2015EDL8060

Efficient anchor graph hashing with data-dependent anchor selection

Hiroaki Takebe, Yusuke Uehara, Seiichi Uchida

Real world robotics

Research output: Contribution to journal › Article › peer-review

3 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 language	English
Pages (from-to)	2030-2033
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E98D
Issue number	11
DOIs	https://doi.org/10.1587/transinf.2015EDL8060
Publication status	Published - Nov 2015

All Science Journal Classification (ASJC) codes

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

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10.1587/transinf.2015EDL8060

Cite this

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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.",

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AB - 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.

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Efficient anchor graph hashing with data-dependent anchor selection

Abstract

All Science Journal Classification (ASJC) codes

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