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Laplacian Co-hashing of Terms and Documents

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Advances in Information Retrieval (ECIR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5993))

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Abstract

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. In this paper, we introduce the novel problem of co-hashing where both documents and terms are hashed simultaneously according to their semantic similarities. Furthermore, we propose a novel algorithm Laplacian Co-Hashing (LCH) to solve this problem which directly optimises the Hamming distance.

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References

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Author information

Authors and Affiliations

  1. School of Business, Economics and Informatics Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK

    Dell Zhang & Jinsong Lu

  2. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Jun Wang

  3. State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, 100 Zijinggang Road, 310058, China

    Deng Cai

Authors
  1. Dell Zhang
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  2. Jun Wang
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  3. Deng Cai
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  4. Jinsong Lu
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Editor information

Editors and Affiliations

  1. Adaptive Information Cluster, Dublin City University, Dublin, 9, Ireland

    Cathal Gurrin

  2. The Open University, Walton Hall, MK7 6HF, Milton Keynes, UK

    Yulan He

  3. Microsoft Research Ltd, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK

    Gabriella Kazai

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Udo Kruschwitz

  5. The Open University, Walton Hall, Milton Keynes, UK

    Suzanne Little

  6. University of London, London, UK

    Thomas Roelleke

  7. Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  8. Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, UK

    Keith van Rijsbergen

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhang, D., Wang, J., Cai, D., Lu, J. (2010). Laplacian Co-hashing of Terms and Documents. In: Gurrin, C., et al. Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_51

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  • DOI: https://doi.org/10.1007/978-3-642-12275-0_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12274-3

  • Online ISBN: 978-3-642-12275-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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