We introduce an algorithm to reduce large data sets using so-called digital nets, which are well distributed point sets in the unit cube. These point sets together with weights, which depend on the data set, are used to represent the data. We show that this can be used to reduce the computational effort needed in finding good parameters in machine learning algorithms. To illustrate our method we provide some numerical examples for neural networks.

中文翻译：

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一种用于机器学习的准蒙特卡罗数据压缩算法

我们引入了一种使用所谓的数字网络来减少大型数据集的算法，这些网络是单位立方体中分布良好的点集。这些点集与取决于数据集的权重一起用于表示数据。我们表明，这可用于减少在机器学习算法中寻找良好参数所需的计算工作量。为了说明我们的方法，我们提供了一些神经网络的数值例子。