A unified approach to constructing correlation coefficients between random variables. (English) Zbl 1444.62063
Measuring the strength of correlation between two variables or two sets of variables is of vital importance in machine learning. This paper proposes the unified index of correlation between two continuous random variables \(X\) and \(Y\) which leads to new measures of correlations and subsumes some of the existing measures such as the Pearson correlation coefficient and Gini correlation coefficient and its extensions widely employed in random forest theory. The proposed measure is validated on three examples.
Reviewer: Denis Sidorov (Irkutsk)
MSC:
| 62H05 | Characterization and structure theory for multivariate probability distributions; copulas |
| 62H20 | Measures of association (correlation, canonical correlation, etc.) |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 68T05 | Learning and adaptive systems in artificial intelligence |
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