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    Self-taught hashing for fast similarity search

    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.

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    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: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 30 May 2013 10:13
    Last Modified: 02 Dec 2016 13:26
    URI: https://eprints.bbk.ac.uk/id/eprint/7088

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