The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Supervised hashing with latent factor models
Abstract
Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.
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Cites
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
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Locality-sensitive hashing scheme based on p-stable distributions
Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. S. (2004) · Proceedings of the twentieth annual symposium on Computational geometry · doi:10.1145/997817.997857 · Cited by 2,936
Proceedings of the International Joint Conference on Artificial Intelligence 2007
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Cited by
HashNet: Deep Learning to Hash by Continuation
Cao, Z., Long, M., Wang, J., & Yu, P. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.598 · Cited by 777
Deep Hashing Network for Efficient Similarity Retrieval
Zhu, H., Long, M., Wang, J., & Cao, Y. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10235 · Cited by 664
Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization
Zhang, D., & Li, W.-J. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8995 · Cited by 616
Self-citationSemantics-preserving hashing for cross-view retrieval
Lin, Z., Ding, G., Hu, M., & Wang, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299011 · Cited by 542
Radisic, A. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17117444 · Cited by 462
Metadata
- Authors
- Zhang, P., Zhang, W., Li, W.-J., & Guo, M.
- Venue
- Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
- Publisher
- ACM
- Published
- 2014-07-03
- Pages
- 173-182
- OpenAlex
- W1996936615
- Cited by
- 271
- References
- 44
- FWCI
- 10.81
- Type
- conference paper
- Language
- en
- Open access
- closed
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