This study proposes a novel approach for solving the PU learning problem based on an anomaly-detection strategy. Latent encodings extracted from positive-labeled data are linearly combined to acquire new samples. These new samples are used as embeddings to increase the density of positive-labeled data and, thus, define a boundary that approximates the positive class. The further a sample is from the boundary the more it is considered as a negative sample. Once a set of negative samples is obtained, the PU learning problem reduces to binary classification. The approach, named Dens-PU due to its reliance on the density of positive-labeled data, was evaluated using benchmark image datasets, and state-of-the-art results were attained.

翻译：本研究提出了一种基于异常检测策略解决PU学习问题的新方法。该方法通过线性组合从正标注数据中提取的潜在编码来生成新样本，并将这些新样本作为嵌入以增加正标注数据的密度，从而构建逼近正类别的决策边界。样本离边界越远，被视为负样本的可能性越高。一旦获得负样本集，PU学习问题便简化为二分类任务。该方法因依赖正标注数据密度而命名为Dens-PU，在基准图像数据集上取得了当前最优的性能结果。