Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. We apply the locally purified state tensor network to the positive unlabeled learning problem and test our model on the MNIST image and 15 categorical/mixed datasets. On the MNIST dataset, we obtain close to the state-of-the-art results even with very few labeled positive samples. We significantly improve the state-of-the-art on categorical datasets. Further, we show that the agreement fraction between outputs of different models on unlabeled samples is a good indicator of the model's performance. Finally, our method can generate new positive and negative instances, which we demonstrate on simple synthetic datasets.

翻译：积极的无标签学习是一个二进制分类问题,有正和无标签的数据。 在负面标签成本昂贵或无法获取的领域中,例如药品和个性化广告中,这是一个常见的二进制分类问题。 我们将本地净化状态的强项网络应用于积极的无标签学习问题,并测试我们关于MNIST图像和15个绝对/混合数据集的模型。 在MNIST数据集中,我们接近于最先进的结果,即使标签为正的样本很少。 我们大大改进了绝对数据集方面的最新水平。 此外,我们表明,在未贴标签样本上的不同模型输出结果之间的一致是该模型性能的良好指标。 最后,我们的方法可以产生新的正和负实例,我们在简单的合成数据集中展示这些实例。</s>