论文标题
针对足够数据的分析定义
Towards an Analytical Definition of Sufficient Data
论文作者
论文摘要
我们表明，对于越来越复杂的五个数据集中的每个数据集，某些培训样本比其他培训样本更有信息。这些样本可以通过分析其在降低尺寸空间中相对于类的质心来确定训练的先验训练。具体而言，我们证明了较近的质心的样本不如最远的样本提供信息。对于所有五个数据集，我们表明在整个培训集中的培训和排除最接近每个班级质心的数据的2％时，培训之间没有统计学上的显着差异。
We show that, for each of five datasets of increasing complexity, certain training samples are more informative of class membership than others. These samples can be identified a priori to training by analyzing their position in reduced dimensional space relative to the classes' centroids. Specifically, we demonstrate that samples nearer the classes' centroids are less informative than those that are furthest from it. For all five datasets, we show that there is no statistically significant difference between training on the entire training set and when excluding up to 2% of the data nearest to each class's centroid.
