The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Self-taught hashing for fast similarity search
Abstract
The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Although some recently proposed techniques are able to generate high-quality codes for documents known in advance, obtaining the codes for previously unseen documents remains to be a very challenging problem. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then train l classifiers via supervised learning to predict the l-bit code for any query document unseen before. Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly.
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Cited by
Shen, F., Shen, C., Liu, W., & Shen, H. T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298598 · Cited by 1,231
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Song, J., Yang, Y., Yang, Y., Huang, Z., & Shen, H. T. (2013) · Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2463676.2465274 · Cited by 620
Semantics-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
Self-citationFast Supervised Hashing with Decision Trees for High-Dimensional Data
Lin, G., Shen, C., Shi, Q., van den Hengel, A., & Suter, D. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.253 · Cited by 422
Differentially Private High-Dimensional Approximate Range Counting, Revisited
Wang, J., Shen, H. T., Song, J., & Ji, J. (2014) · arXiv (Cornell University) · doi:10.4230/lipics.cpm.2023.20 · Cited by 370
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Metadata
- Authors
- Zhang, D., Wang, J., Cai, D., & Lu, J.
- Venue
- Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2010-07-19
- Pages
- 18-25
- OpenAlex
- W1835419070
- Cited by
- 347
- References
- 52
- FWCI
- 14.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, D., Wang, J., Cai, D., & Lu, J. (2010). Self-taught hashing for fast similarity search. Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1835449.1835455
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