An Optimal Transport Approach for Selecting a Representative Subsample with Application in Efficient Kernel Density Estimation

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this article, we study model-free subsam...

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Vydané v:	Journal of computational and graphical statistics Ročník 32; číslo 1; s. 329 - 339
Hlavní autori:	Zhang, Jingyi, Meng, Cheng, Yu, Jun, Zhang, Mengrui, Zhong, Wenxuan, Ma, Ping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 02.01.2023 Taylor & Francis Ltd
Predmet:	Adaptive algorithms Clustering Convergence Density estimation Empirical analysis Inverse transform sampling Kernels Optimal transport Population distribution Probability density functions Star discrepancy Subsampling
ISSN:	1061-8600, 1537-2715
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