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Found 3 papers, 1 papers with code
Positive-Unlabeled Learning with Non-Negative Risk Estimator
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning.
Class-prior Estimation for Learning from Positive and Unlabeled Data
Under the assumption that an additional labeled dataset is available, the class prior can be estimated by fitting a mixture of class-wise data distributions to the unlabeled data distribution.
Analysis of Learning from Positive and Unlabeled Data
We next analyze the excess risk when the class prior is estimated from data, and show that the classification accuracy is not sensitive to class prior estimation if the unlabeled data is dominated by the positive data (this is naturally satisfied in inlier-based outlier detection because inliers are dominant in the unlabeled dataset).
