A Two-Step Classification Method Based on Collaborative Representation for Positive and Unlabeled Learning

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.