论文标题
生物识别培训者：超高维，多级合成数据生成器，以模仿生物特征空间
BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space
论文作者
论文摘要
缺乏可自由获得的（现实或合成）高或超高维度的多级数据集可能会阻碍对特征筛查的快速增长的研究，尤其是在生物识别技术领域，在这种情况下，此类数据集的使用很常见。本文报告了一个名为Biometricblender的Python软件包，它是一种超高维，多级合成数据生成器，可基准多种功能筛选方法。在数据生成过程中，用户可以控制混合特征的总体实用性和相互关系，因此合成特征空间能够模仿实际生物识别数据集的关键属性。
The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common. This paper reports a Python package called BiometricBlender, which is an ultra-high dimensional, multi-class synthetic data generator to benchmark a wide range of feature screening methods. During the data generation process, the overall usefulness and the intercorrelations of blended features can be controlled by the user, thus the synthetic feature space is able to imitate the key properties of a real biometric dataset.
