Abstract
Coresets can be described as a compact subset such that models trained on coresets will also provide a good fit with models trained on full data set. By using coresets, we can scale down a big data to a tiny one in order to reduce the computational cost of a machine learning problem. In recent years, data scientists have investigated various methods to create coresets. The two state-of-the-art algorithms have been proposed in 2018 are ProTraS by Ros & Guillaume and Lightweight Coreset by Bachem et al. In this paper, we briefly introduce these two algorithms and make a comparison between them to find out the benefits and drawbacks of each one.
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Acknowledgment
This work is supported by a project with the Department of Science and Technology, Ho Chi Minh City, Vietnam (contract with HCMUT No. 42/2019/HD-QPTKHCN, dated 11/7/2019).
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Hoang, N.L., Dang, T.K., Trang, L.H. (2019). A Comparative Study of the Use of Coresets for Clustering Large Datasets. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_4
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DOI: https://doi.org/10.1007/978-3-030-35653-8_4
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