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Efficient Context-Aware K-Nearest Neighbor Search

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Advances in Information Retrieval (ECIR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10772))

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Abstract

We develop a context-sensitive and linear-time K-nearest neighbor search method, wherein the test object and its neighborhood (in the training dataset) are required to share a similar structure via establishing bilateral relations. Our approach particularly enables to deal with two types of irregularities: (i) when the (test) objects are outliers, i.e. they do not belong to any of the existing structures in the (training) dataset, and (ii) when the structures (e.g. classes) in the dataset have diverse densities. Instead of aiming to capture the correct underlying structure of the whole data, we extract the correct structure in the neighborhood of the test object, which leads to computational efficiency of our search strategy. We investigate the performance of our method on a variety of real-world datasets and demonstrate its superior performance compared to the alternatives.

Some part of the work is done at NAVER LABS Europe.

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Notes

  1. 1.

    Note that our approach is orthogonal to the methods such as Minimax distances [4], where they can be conbined and used together.

  2. 2.

    Note that if we aim to investigate the algorithm for different K in an incremental manner, then for each K, we only need to compute the \(K^{th}\) nearest neighbor, as this search has been already performed for \(K-1\) in the previous step.

  3. 3.

    In our experiments, ENN and IKNN perform very similarly, as both does not provide any outlier detection mechanism. We only report the scores of IKNN.

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Author information

Authors and Affiliations

  1. Telecom ParisTech, Paris, France

    Mostafa Haghir Chehreghani

  2. Chalmers University of Technology, Gothenburg, Sweden

    Morteza Haghir Chehreghani

Authors
  1. Mostafa Haghir Chehreghani
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  2. Morteza Haghir Chehreghani
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Corresponding author

Correspondence to Morteza Haghir Chehreghani .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  2. LIP6 – UPMC/CNRS, University Pierre et Marie Curie, Paris, France

    Benjamin Piwowarski

  3. University of Glasgow, Glasgow, United Kingdom

    Leif Azzopardi

  4. Technical University of Vienna, Vienna, Austria

    Allan Hanbury

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Haghir Chehreghani, M., Haghir Chehreghani, M. (2018). Efficient Context-Aware K-Nearest Neighbor Search. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_35

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  • DOI: https://doi.org/10.1007/978-3-319-76941-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76940-0

  • Online ISBN: 978-3-319-76941-7

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