论文标题
在建模世界发展数据中，对多回归和PCA的鲁棒性和数值稳定性的研究调查
Investigation of robustness and numerical stability of multiple regression and PCA in modeling world development data
论文作者
论文摘要
用于对数据进行建模和未标记的数据进行建模的流行方法，多个回归和PCA已用于大量数据集的研究中。在这项调查中，我们试图通过对世界发展数据进行拟合，以其复杂性和高维度臭名昭著，从而突破这两种方法的限制。我们使用它们的矩阵条件编号和捕获数据集方差的能力来评估两种方法的鲁棒性和数值稳定性。结果表明，从两种方法的角度来看，这两种方法的性能差，但仍然可以捕获某些定性见解。
Popular methods for modeling data both labelled and unlabeled, multiple regression and PCA has been used in research for a vast number of datasets. In this investigation, we attempt to push the limits of these two methods by running a fit on world development data, a set notorious for its complexity and high dimensionality. We assess the robustness and numerical stability of both methods using their matrix condition number and ability to capture variance in the dataset. The result indicates poor performance from both methods from a numerical standpoint, yet certain qualitative insights can still be captured.
