arXiv:2502.21194v3 Announce Type: replace-cross Abstract: We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assumed that the source data is partially observable: only samples from the positive class and from the whole population are available (PU learning scenario). We introduce a novel direct estimator of a class prior which avoids estimation of posterior probabilities in both populations and has a simple geometric interpretation. It is based on a distribution matching technique together with kernel embeddi
This paper represents continued progress in machine learning research, specifically addressing challenges in learning from incomplete datasets, a common problem in real-world AI applications.
Improved prior shift estimation for positive unlabeled (PU) data can enhance the accuracy and robustness of AI models, particularly in domains where comprehensive labeled data is scarce or expensive to acquire.
This new direct estimator provides a more efficient and geometrically interpretable method for handling class prior shifts in PU learning, potentially leading to more reliable AI systems.
- · AI/ML researchers
- · Data scientists
- · Industries with imbalanced or incomplete datasets (e.g., fraud detection, medica
- · Traditional, less robust PU learning methods
More accurate classification models can be developed with less fully labeled data.
This could accelerate AI deployment in sectors with data privacy concerns or ethical constraints on data collection.
Reduced data labeling costs might democratize advanced AI application development, fostering broader innovation.
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