Multi-view Positive and Unlabeled Learning
; Proceedings of the Asian Conference on Machine Learning, PMLR 25:555-570, 2012.
Learning with Positive and Unlabeled instances (PU learning) arises widely in information retrieval applications. To address the unavailability issue of negative instances, most existing PU learning approaches require to either identify a reliable set of negative instances from the unlabeled data or estimate probability densities as an intermediate step. However, inaccurate negative-instance identification or poor density estimation may severely degrade overall performance of the final predictive model. To this end, we propose a novel PU learning method based on density ratio estimation without constructing any sets of negative instances or estimating any intermediate densities. To further boost PU learning performance, we extend our proposed learning method in a multi-view manner by utilizing multiple heterogeneous sources. Extensive experimental studies demonstrate the effectiveness of our proposed methods, especially when positive labeled data are limited.