Relating Leverage Scores and Density using Regularized Christoffel Functions

Abstract : Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature. Yet, the very nature of this quantity is barely understood. Borrowing ideas from the orthogonal polynomial literature, we introduce the regularized Christoffel function associated to a positive definite kernel. This uncovers a variational formulation for leverage scores for kernel methods and allows to elucidate their relationships with the chosen kernel as well as population density. Our main result quantitatively describes a decreasing relation between leverage score and population density for a broad class of kernels on Euclidean spaces. Numerical simulations support our findings.
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Contributeur : Edouard Pauwels <>
Soumis le : lundi 21 mai 2018 - 10:49:59
Dernière modification le : lundi 18 juin 2018 - 10:32:24


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  • HAL Id : hal-01796591, version 1


Edouard Pauwels, Francis Bach, Jean-Philippe Vert. Relating Leverage Scores and Density using Regularized Christoffel Functions. 2018. 〈hal-01796591〉



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