A Generalization of the Pearson Correlation to Riemannian Manifolds

Patrick Michl

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arXiv · 2006.04215

A Generalization of the Pearson Correlation to Riemannian Manifolds

Patrick Michl

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Abstract

The increasing application of deep-learning is accompanied by a shift towards highly non-linear statistical models. In terms of their geometry it is natural to identify these models with Riemannian manifolds. The further analysis of the statistical models therefore raises the issue of a correlation measure, that in the cutting planes of the tangent spaces equals the respective Pearson correlation and extends to a correlation measure that is normalized with respect to the underlying manifold. In this purpose the article reconstitutes elementary properties of the Pearson correlation to successively derive a linear generalization to multiple dimensions and thereupon a nonlinear generalization to principal manifolds, given by the Riemann-Pearson Correlation.

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Patrick Michl. 2020-06-07. A Generalization of the Pearson Correlation to Riemannian Manifolds. https://arxiv.org/abs/2006.04215

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