Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison
<|reference_start|>Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison: Low-discrepancy points (also called Quasi-Monte Carlo points) are deterministically and cleverly chosen point sets in the unit cube, which provide an approximation of the uniform distribution. We explore two methods based on such low-discrepancy points to reduce large data sets in order to train neural networks. The first one is the method of Dick and Feischl [4], which relies on digital nets and an averaging procedure. Motivated by our experimental findings, we construct a second method, which again uses digital nets, but Voronoi clustering instead of averaging. Both methods are compared to the supercompress approach of [14], which is a variant of the K-means clustering algorithm. The comparison is done in terms of the compression error for different objective functions and the accuracy of the training of a neural network.<|reference_end|>
@article{göttlich2024using,
    title={Using Low-Discrepancy Points for Data Compression in Machine Learning:
  An Experimental Comparison},
    author={Simone G"ottlich, Jacob Heieck, Andreas Neuenkirch},
    journal={arXiv preprint arXiv:2407.07450},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07450},
    primaryClass={stat.ML cs.LG}
}