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Classification with many classes: Challenges and pluses

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  • Abramovich, Felix
  • Pensky, Marianna

Abstract

The objective of the paper is to study accuracy of multi-class classification in high-dimensional setting, where the number of classes is also large (“large L, large p, small n” model). While this problem arises in many practical applications and many techniques have been recently developed for its solution, to the best of our knowledge nobody provided a rigorous theoretical analysis of this important setup. The purpose of the present paper is to fill in this gap.

Suggested Citation

  • Abramovich, Felix & Pensky, Marianna, 2019. "Classification with many classes: Challenges and pluses," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:jmvana:v:174:y:2019:i:c:s0047259x19302763
    DOI: 10.1016/j.jmva.2019.104536
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    References listed on IDEAS

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    1. Rui Pan & Hansheng Wang & Runze Li, 2016. "Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 169-179, March.
    2. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
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    Cited by:

    1. Michael O. Olusola & Sydney I. Onyeagu, 2020. "On the binary classification problem in discriminant analysis using linear programming methods," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(1), pages 119-130.
    2. Kim, Tae Kyung & Kim, Sukyung & Won, Myoungsoo & Lim, Jong-Hwan & Yoon, Sukhee & Jang, Keunchang & Lee, Kye-Han & Park, Yeong Dae & Kim, Hyun Seok, 2021. "Utilizing machine learning for detecting flowering in mid-range digital repeat photography," Ecological Modelling, Elsevier, vol. 440(C).

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