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• W2912106686 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 approximating 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.
• W2912106686 created "2019-02-21" @default.
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• W2912106686 date "2019-03-07" @default.
• W2912106686 modified "2023-10-06" @default.
• W2912106686 title "The empirical Christoffel function with applications in data analysis" @default.
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• W2912106686 doi "https://doi.org/10.1007/s10444-019-09673-1" @default.
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