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Small Samples of Multidimensional Feature Vectors

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Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

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Abstract

A small sample of multidimensional feature vectors appears when the number of features is much greater than the number of objects (feature vectors).

For example, such circumstances appear typically in genetic data sets. In such cases, feature clustering can become a useful tool in classification or prognosis tasks. Feature clustering can be performed through the minimization of the convex and piecewise linear (CPL) criterion functions.

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Acknowledgments

The presented study was supported by the grant S/WI/2/2020 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Białystok University of Technology, Białystok, Poland

    Leon Bobrowski

  2. Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland

    Leon Bobrowski

Authors
  1. Leon Bobrowski
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Correspondence to Leon Bobrowski .

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Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

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Bobrowski, L. (2020). Small Samples of Multidimensional Feature Vectors. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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