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On uniform concentration bounds for Bi-clustering by using the Vapnik–Chervonenkis theory

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  • Chakraborty, Saptarshi
  • Das, Swagatam

Abstract

Bi-clustering refers to the task of partitioning the rows and columns of a data matrix simultaneously. Although empirically useful, the theoretical aspects of bi-clustering techniques have not been studied in-depth. We present a framework for investigating the statistical guarantees behind the sparse bi-clustering algorithm by using the Vapnik–Chervonenkis (VC) theory.

Suggested Citation

  • Chakraborty, Saptarshi & Das, Swagatam, 2021. "On uniform concentration bounds for Bi-clustering by using the Vapnik–Chervonenkis theory," Statistics & Probability Letters, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:stapro:v:175:y:2021:i:c:s016771522100064x
    DOI: 10.1016/j.spl.2021.109102
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    References listed on IDEAS

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    1. Gallegos, María Teresa & Ritter, Gunter, 2013. "Strong consistency of k-parameters clustering," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 14-31.
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    4. Yoshikazu Terada, 2014. "Strong Consistency of Reduced K-means Clustering," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 913-931, December.
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