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On a Weakly Supervised Classification Problem

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Analysis of Images, Social Networks and Texts (AIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13217))

Abstract

We consider a weakly supervised classification problem. It is a classification problem where the target variable can be unknown or uncertain for some subset of samples. This problem appears when the labeling is impossible, time-consuming, or expensive. Noisy measurements and lack of data may prevent accurate labeling. Our task is to build an optimal classification function. For this, we construct and minimize a specific objective function, which includes the fitting error on labeled data and a smoothness term. Next, we use covariance and radial basis functions to define the degree of similarity between points. The further process involves the repeated solution of an extensive linear system with the graph Laplacian operator. To speed up this solution process, we introduce low-rank approximation techniques. We call the resulting algorithm WSC-LR. Then we use the WSC-LR algorithm for analysis CT brain scans to recognize ischemic stroke disease. We also compare WSC-LR with other well-known machine learning algorithms.

The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no FWNF-2022-0015). The work was partly supported by RFBR grant 19-29-01175. A. Litvinenko was supported by funding from the Alexander von Humboldt Foundation.

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Author information

Authors and Affiliations

  1. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Vladimir Berikov

  2. Novosibirsk State University, Novosibirsk, Russia

    Vladimir Berikov

  3. RWTH Aachen, Aachen, Germany

    Alexander Litvinenko

  4. Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia

    Igor Pestunov & Yuriy Sinyavskiy

Authors
  1. Vladimir Berikov
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  2. Alexander Litvinenko
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  3. Igor Pestunov
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  4. Yuriy Sinyavskiy
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Corresponding author

Correspondence to Vladimir Berikov .

Editor information

Editors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Evgeny Burnaev

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Skolkovo Institute of Science and Technology, Moscow, Russia

    Sergei Ivanov

  4. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrei Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  9. LORIA, Campus Scientifique, Vandœuvre lès Nancy, France

    Amedeo Napoli

  10. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Aalto University, Espoo, Finland

    Jari Saramäki

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Yandex LLC, Moscow, Russia

    Evgenii Tsymbalov

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Berikov, V., Litvinenko, A., Pestunov, I., Sinyavskiy, Y. (2022). On a Weakly Supervised Classification Problem. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_26

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  • DOI: https://doi.org/10.1007/978-3-031-16500-9_26

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