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.
KeywordsObesity Covariance Sammon
- 1.D. Freedman, R. Pisani, and R. Purves. Statistics. W. W. Norton & Company, New York, 2007.Google Scholar
- 3.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