Positive Unlabeled (PU) learning refers to the task of learning a binary
classifier given a few labeled positive samples, and a set of unlabeled samples
(which could be positive or negative). In this paper, we propose a novel PU
learning framework, that starts by learning a feature space through
pretext-invariant representation learning and then applies pseudo-labeling to
the unlabeled examples, leveraging the concentration property of the
embeddings. Overall, our proposed approach handily outperforms state-of-the-art
PU learning methods across several standard PU benchmark datasets, while not
requiring a-priori knowledge or estimate of class prior. Remarkably, our method
remains effective even when labeled data is scant, where most PU learning
algorithms falter. We also provide simple theoretical analysis motivating our
proposed algorithms and establish generalization guarantee for our approach.

我们提出了一种新的正未标化学习框架，通过预定义无关的表示学习来学习特征空间，然后采用伪标记技术将未标注样本进行分类，利用嵌入的集中性质。该方法在多个标准正未标化基准数据集上明显优于现有的方法，并且不需要先验知识或类先验的估计。值得注意的是，即使标注数据稀缺，我们的方法仍然有效，而大多数正未标化学习算法则表现不佳。我们还提供了简单的理论分析，解释了我们所提出算法的动机，并为我们的方法建立了泛化保证。