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Calibrating Distance Metrics Under Uncertainty

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13715))

Abstract

Estimating distance metrics for given data samples is essential in machine learning algorithms with various applications. Accurately determining the metric becomes impossible if there are observation noises or missing values. In this work, we proposed an approach to calibrating distance metrics. Compared with standard practices that primarily reside on data imputation, our proposal makes fewer assumptions about the data. It provides a solid theoretical guarantee in improving the quality of the estimate. We developed a simple, efficient, yet effective computing procedure that scales up to realize the calibration process. The experimental results from a series of empirical evaluations justified the benefits of the proposed approach and demonstrated its high potential in practical applications.

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Notes

  1. 1.

    We set \(\mu = \max \left\{ d^{0}_{ij}\right\} \) and \(\epsilon =0.02\) in the study.

  2. 2.

    Implementation downloaded from http://optml.mit.edu/software.html.

  3. 3.

    Implementation downloaded from https://candes.su.domains/software/.

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Acknowledgments

We thank the reviewers for the helpful comments. The work is supported by Guangdong Basic and Applied Basic Research Foundation (2021A1515011825), Shenzhen Science and Technology Program (CUHKSZWDZC0004), and Shenzhen Research Institute of Big Data.

Author information

Authors and Affiliations

  1. The Chinese University of Hong Kong, Shenzhen, China

    Wenye Li & Fangchen Yu

  2. Shenzhen Research Institute of Big Data, Shenzhen, China

    Wenye Li

Authors
  1. Wenye Li
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  2. Fangchen Yu
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Corresponding author

Correspondence to Wenye Li .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Li, W., Yu, F. (2023). Calibrating Distance Metrics Under Uncertainty. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13715. Springer, Cham. https://doi.org/10.1007/978-3-031-26409-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-26409-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26408-5

  • Online ISBN: 978-3-031-26409-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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