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SCIENCE

The Claim

Correlation entropy is used as a statistical feature in the analysis of datasets.

Verified by Lenz ·

The Short Version

Scholarly studies show correlation entropy has been extracted as a quantitative feature—especially in time-series and independence tests—confirming that the method is indeed used in data analysis. The claim does not specify prevalence, so documented albeit specialized usage suffices. Its application, however, is niche and not part of most mainstream statistical or machine-learning toolkits.

Caveats

  • Correlation entropy is specialized; its use is largely confined to nonlinear dynamics, serial-independence or time-series studies.
  • Some sources conflate correlation entropy with broader entropy measures such as mutual information; the two are related but not identical.
  • Mainstream libraries rarely implement correlation entropy, so it should not be assumed to be a standard feature across general datasets.

The Receipts

  1. Entropy Correlation and Its Impacts on Data Aggregation in a ...

    PMC (PubMed Central)

  2. Applications of Entropy in Data Analysis and Machine Learning: A Review - PMC

    PMC

  3. Applications of Common Entropy for Causal Inference

    NSF PAR

  4. Entropy and its Relationship with Statistics

    Sandia National Laboratories

  5. Definition and Time Evolution of Correlations in Classical Statistical Mechanics - PMC

    PMC

  6. Applications of Entropy in Data Analysis and Machine Learning: A Review - arXiv

    arXiv

  7. Correlations of Cross-Entropy Loss in Machine Learning - PubMed

    PubMed

  8. sklearn.feature_selection.mutual_info_classif

    scikit-learn Documentation

  9. Lecture 4: Measures of Correlation and Dependence - Mutual Information and Maximal Information Coefficient

    University of Oxford Statistics

  10. [math-ph/0001024] The Boltzmann/Shannon entropy as a measure of correlation - arXiv

    arXiv

+ 10 more sources — see the full list on Lenz

Filed Under

correlation entropydatasets