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Correlation

Chapter

Abstract

Correlation quantifies the relationship between features. Linear correlation methods are robust and computationally efficient but detect only linear dependencies. Nonlinear correlation methods are able to detect nonlinear dependencies but need to be carefully parametrized. As a popular example for nonlinear correlation we present the chi-square test for independence that can be applied to continuous features using histogram counts. Nonlinear correlation can also be quantified by the regression validation error. Correlation does not imply causality, so correlation analysis may reveal spurious correlations. If the underlying features are known, then spurios correlations may be compensated by partial correlation methods.

Keywords

Forest Fire Causal Connection Nonlinear Dependency Spurious Correlation Conditional Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    T. A. Runkler. Fuzzy histograms and fuzzy chi–squared tests for independence. In IEEE International Conference on Fuzzy Systems, volume 3, pages 1361–1366, Budapest, July 2004.Google Scholar
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    H. A. Simon. Spurious correlation: A causal interpretation. Journal of the American Statistical Association, 49:467–479, 1954.MATHGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2016

Authors and Affiliations

  1. 1.Siemens AGMünchenGermany

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