A Unified Approximate Nearest Neighbor Search Scheme by Combining Data Structure and Hashing
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 681-687.
Abstract
Nowadays, Nearest Neighbor Search becomes more and more important when facing the challenge of big data. Traditionally, to solve this problem, researchers mainly focus on building effective data structures such as hierarchical k-means tree or using hashing methods to accelerate the query process. In this paper, we propose a novel unified approximate nearest neighbor search scheme to combine the advantages of both the effective data structure and the fast Hamming distance computation in hashing methods. In this way, the searching procedure can be further accelerated. Computational complexity analysis and extensive experiments have demonstrated the effectiveness of our proposed scheme.
@inproceedings{ijcai2013p107,
title = {A Unified Approximate Nearest Neighbor Search Scheme by Combining Data Structure and Hashing},
author = {Zhang, Debing and Yang, Genmao and Hu, Yao and Jin, Zhongming and Cai, Deng and He, Xiaofei},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {681--687},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/107},
}
TY - CPAPER TI - A Unified Approximate Nearest Neighbor Search Scheme by Combining Data Structure and Hashing AU - Zhang, Debing AU - Yang, Genmao AU - Hu, Yao AU - Jin, Zhongming AU - Cai, Deng AU - He, Xiaofei T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 681 EP - 687 UR - https://ijcai.org/proceedings/2013/107 ER -Copied
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