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arXiv · 2407.07450

Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison

Simone Göttlich·Jacob Heieck·Andreas Neuenkirch
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Abstract

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.

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Simone Göttlich, Jacob Heieck, Andreas Neuenkirch. 2024-07-10. Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison. https://arxiv.org/abs/2407.07450

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