  People learn to discriminate between classes without explicit exposure to
negative examples. On the contrary, traditional machine learning algorithms
often rely on negative examples, otherwise the model would be prone to collapse
and always-true predictions. Therefore, it is crucial to design the learning
objective which leads the model to converge and to perform predictions
unbiasedly without explicit negative signals. In this paper, we propose a
Collectively loss function to learn from only Positive and Unlabeled data
(cPU). We theoretically elicit the loss function from the setting of PU
learning. We perform intensive experiments on the benchmark and real-world
datasets. The results show that cPU consistently outperforms the current
state-of-the-art PU learning methods.
