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• W2951594960 abstract "We illustrate the potential applications in machine learning of the Christoffel function, or more precisely, its empirical counterpart associated with a counting measure uniformly supported on a finite set of points. Firstly, we provide a thresholding scheme which allows to approximate the support of a measure from a finite subset of its moments with strong asymptotic guaranties. Secondly, we provide a consistency result which relates the empirical Christoffel function and its population counterpart in the limit of large samples. Finally, we illustrate the relevance of our results on simulated and real world datasets for several applications in statistics and machine learning: (a) density and support estimation from finite samples, (b) outlier and novelty detection and (c) affine matching." @default.
• W2951594960 created "2019-06-27" @default.
• W2951594960 creator A5006670993 @default.
• W2951594960 creator A5067908150 @default.
• W2951594960 date "2017-01-11" @default.
• W2951594960 modified "2023-09-30" @default.
• W2951594960 title "The empirical Christoffel function with applications in data analysis" @default.
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• W2951594960 hasPublicationYear "2017" @default.
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