  Positive unlabeled learning is a binary classification problem with positive
and unlabeled data. It is common in domains where negative labels are costly or
impossible to obtain, e.g., medicine and personalized advertising. Most
approaches to positive unlabeled learning apply to specific data types (e.g.,
images, categorical data) and can not generate new positive and negative
samples. This work introduces a feature-space distance-based tensor network
approach to the positive unlabeled learning problem. The presented method is
not domain specific and significantly improves the state-of-the-art results on
the MNIST image and 15 categorical/mixed datasets. The trained tensor network
model is also a generative model and enables the generation of new positive and
negative instances.
