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Affinity Propagation Based Closed-Form Semi-supervised Metric Learning Framework

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Book cover Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11139))

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Abstract

Recent state-of-the-art deep metric learning approaches require large number of labeled examples for their success. They cannot directly exploit unlabeled data. When labeled data is scarce, it is very essential to be able to make use of additionally available unlabeled data to learn a distance metric in a semi-supervised manner. Despite the presence of a few traditional, non-deep semi-supervised metric learning approaches, they mostly rely on the min-max principle to encode the pairwise constraints, although there are a number of other ways as offered by traditional weakly-supervised metric learning approaches. Moreover, there is no flow of information from the available pairwise constraints to the unlabeled data, which could be beneficial. This paper proposes to learn a new metric by constraining it to be close to a prior metric while propagating the affinities among pairwise constraints to the unlabeled data via a closed-form solution. The choice of a different prior metric thus enables encoding of the pairwise constraints by following formulations other than the min-max principle.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India

    Ujjal Kr Dutta & C. Chandra Sekhar

Authors
  1. Ujjal Kr Dutta
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  2. C. Chandra Sekhar
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Corresponding author

Correspondence to Ujjal Kr Dutta .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Kr Dutta, U., Chandra Sekhar, C. (2018). Affinity Propagation Based Closed-Form Semi-supervised Metric Learning Framework. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_55

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  • DOI: https://doi.org/10.1007/978-3-030-01418-6_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01417-9

  • Online ISBN: 978-3-030-01418-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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