Zhang, Dell and Wang, J. and Cai, D. and Lu, J. (2010) Self-taught hashing for fast similarity search. In: Crestani, F. and Marchand-Maillet, S. and Chen, H.-H. and Efthimiadis, E.N. and Savoy, J. (eds.) Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10. New York, U.S.: ACM Press, pp. 18-25. ISBN 9781450301534.
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.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | Administrator |
| Date Deposited: | 30 May 2013 10:13 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7088 |
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