Sansone Emanuele, De Natale Francesco G B, Zhou Zhi-Hua
IEEE Trans Pattern Anal Mach Intell. 2019 Nov;41(11):2584-2598. doi: 10.1109/TPAMI.2018.2860995. Epub 2018 Jul 30.
Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.
正无标记（PU）学习在各种实际情况中都很有用，在这些情况下，需要从未标记的数据集中学习针对感兴趣类别的分类器，该数据集可能包含异常以及来自未知类别的样本。学习任务可以在统计学习理论框架下被表述为一个优化问题。最近的研究从理论上分析了它的性质和泛化性能，然而，在考虑可扩展性问题上几乎没有做什么工作，特别是当有大量未标记数据可用时。在这项工作中，我们提出了一种新颖的可扩展PU学习算法，该算法在理论上被证明能提供最优解，同时展现出卓越的计算和内存性能。实验评估证实了理论依据，并表明所提出的方法可以成功应用于涉及PU学习的各种现实世界问题。
