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Study on the Overlap in Matrix-Variate Data with Applications in Discriminant Analysis

Author

Listed:
  • Yingying Zhang

    (Western Michigan University)

  • Volodymyr Melnykov

    (The University of Alabama)

  • Xuwen Zhu

    (The University of Alabama)

Abstract

This paper introduces and quantifies the concept of overlap in matrix-variate data. The proposed methodology derives and computes the exact overlap, defined as the total misclassification probability between two clusters. Discriminant functions tailored for matrix-variate data are developed based on a specified level of overlap, and their effectiveness is evaluated. The performance of these classifiers is compared to that of traditional multivariate classifiers across various simulation scenarios, considering different overlap levels, sample sizes, and matrix dimensions. Results from both simulated and real-world datasets demonstrate the superior performance of discriminant functions based on the matrix distribution format.

Suggested Citation

  • Yingying Zhang & Volodymyr Melnykov & Xuwen Zhu, 2025. "Study on the Overlap in Matrix-Variate Data with Applications in Discriminant Analysis," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(2), pages 378-403, August.
    • Handle: RePEc:spr:sankha:v:87:y:2025:i:2:d:10.1007_s13171-025-00406-9
    DOI: 10.1007/s13171-025-00406-9
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