Positive and Unlabeled learning (PU learning) has drawn plenty of attention among researchers over the last few years, where only labeled positive examples and unlabeled examples are available for training a classifier. Many classic techniques for solving PU learning problems belong to the category of “two-step strategy”. However, quite a number of them cannot extract reliable negative examples accurately and often lead to unsatisfactory classification results. In this paper, we propose a two-step learning scheme based on the collaborative representation (CR) for PU learning. In the first step, to handle the deficiency of negative training data, collaborative representation (CR) technique is utilized to identify reliable negative examples from unlabeled training examples. Subsequently, collaborative representation based classification (CRC) framework with \({l}_{2}\)-norm regularization term is applied to perform PU classification. Extensive experiments on both benchmark and real-world datasets were conducted to verify the effectiveness of the proposed method, and the results demonstrate that the two-step CR-based approaches can achieve competitive classification accuracy when compared with both traditional and state-of-the-art techniques in dealing with different PU learning issues.

在过去几年中，正例和未标记学习（PU 学习）引起了研究人员的广泛关注，其中只有标记的正例和未标记的例子可用于训练分类器。许多解决PU学习问题的经典技巧都属于“两步策略”的范畴。然而，其中相当多的方法无法准确提取可靠的负例，往往导致分类结果不理想。在本文中，我们提出了一种基于协作表示（CR）的两步学习方案，用于 PU 学习。第一步，为了解决负面训练数据的不足，利用协同表示（CR）技术从未标记的训练示例中识别可靠的负面示例。随后，\({l}_{2}\) -norm 正则化项用于执行 PU 分类。在基准数据集和真实数据集上进行了大量实验以验证所提出方法的有效性，结果表明，与传统和最先进的方法相比，基于两步 CR 的方法可以实现具有竞争力的分类精度。 - 处理不同 PU 学习问题的艺术技巧。