An Empirical Analysis of KDE-based Generative Models on Small Datasets
2021; Elsevier BV; Volume: 193; Linguagem: Inglês
10.1016/j.procs.2021.10.046
ISSN1877-0509
AutoresEkaterina Plesovskaya, Sergey Ivanov,
Tópico(s)Gaussian Processes and Bayesian Inference
ResumoOne of the approaches to deal with the small dataset problem is synthetic data generation. Kernel density estimation is a common method to approximate the underlying probability distribution of a small dataset. The present paper aims to analyze the generation capability of KDE-based models by evaluating their samples. For this purpose, we introduce a framework for synthetic dataset quality estimation which also accounts for the overfitting of a generative model. The performance of KDE is analyzed on samples from theoretical distributions and real datasets. The results state that KDE generates synthetic samples of a good quality and outperforms its competitors on small datasets.
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