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Improved Spectral Hashing

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Advances in Knowledge Discovery and Data Mining (PAKDD 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6634))

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Abstract

Nearest neighbor search is one of the most fundamental problem in machine learning, machine vision, clustering, information retrieval, etc. To handle a dataset of million or more records, efficient storing and retrieval techniques are needed. Binary code is an efficient method to address these two problems. Recently, the problem of finding good binary code has been formulated and solved, resulting in a technique called spectral hashing [21]. In this work we analyze the spectral hashing, its possible shortcomings and solutions. Experimental results are promising.

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Author information

Authors and Affiliations

  1. IMG lab, NECTEC, 112 Thailand Science Park, Klong 1, Klong Luang, Pathumthani, Thailand

    Sanparith Marukatat & Wasin Sinthupinyo

Authors
  1. Sanparith Marukatat
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  2. Wasin Sinthupinyo
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Editor information

Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, 518055, Shenzhen, China

    Joshua Zhexue Huang

  2. Faculty of Engineering and Information Technology, Center for Quantum Computation and Intelligent Systems, Data Sciences and Knowledge Discovery Lab, University of Technology Sydney, NSW 2007, Sydney, Australia

    Longbing Cao

  3. Department of Computer Science and Engineering, University of Minnesota, MN 55455, Minneapolis, USA

    Jaideep Srivastava

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© 2011 Springer-Verlag Berlin Heidelberg

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Marukatat, S., Sinthupinyo, W. (2011). Improved Spectral Hashing. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20841-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-20841-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20840-9

  • Online ISBN: 978-3-642-20841-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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