We introduce Onion Universe Algorithm (OUA), a novel classification method in ensemble learning. In particular, we show its applicability as a label model for weakly supervised learning. OUA offers simplicity in implementation with minimal assumptions on the data or weak signals. The model is well suited for scenarios where fully labeled data is not available. Our method is built upon geometrical interpretation of the space spanned by weak signals. Our analysis of the high dimensional convex hull structure underlying general set of weak signals bridges geometry with machine learning. Empirical results also demonstrate that OUA works well in practice and compares favorably to best existing label models for weakly supervised learning.

翻译：我们提出了一种新颖的集成学习方法——洋葱宇宙算法（OUA），并重点展示了其作为弱监督学习中标签模型的应用潜力。OUA实现简单，对数据或弱信号的假设极少，特别适用于缺乏完全标注数据的场景。该方法基于弱信号所张成空间的几何解释，通过分析弱信号集合的高维凸包结构，将几何学与机器学习紧密联结。实验结果表明，OUA在实际应用中表现优异，优于现有最优的弱监督学习标签模型。