We demonstrate that Gini coefficients can be used as unified metrics to evaluate many-versus-many (all-to-all) similarity in vector spaces. Our analysis of various image datasets shows that images with the highest Gini coefficients tend to be the most similar to one another, while images with the lowest Gini coefficients are the least similar. We also show that this relationship holds true for vectorized text embeddings from various corpuses, highlighting the consistency of our method and its broad applicability across different types of data. Additionally, we demonstrate that selecting machine learning training samples that closely match the distribution of the testing dataset is far more important than ensuring data diversity. Selection of exemplary and iconic training samples with higher Gini coefficients leads to significantly better model performance compared to simply having a diverse training set with lower Gini coefficients. Thus, Gini coefficients can serve as effective criteria for selecting machine learning training samples, with our selection method outperforming random sampling methods in very sparse information settings.

本文针对向量空间中多对多相似性评估的缺陷，提出使用基尼系数作为统一指标的方法。研究表明，高基尼系数的图像互相之间的相似性较高，而低基尼系数图像的相似性较低。此外，选择与测试数据集分布相匹配的机器学习训练样本比确保数据多样性更为重要，从而显著提升模型性能。 

基尼系数作为评估向量空间中多对多相似性的统一指标