Machine learning can be divided into several areas: supervised learning, unsupervised learning, semi-supervised learning, learning to rank, recommendation systems, etc, etc.
One such area is PU Learning, where only Positive and Unlabeled instances are available.
There are many publications about this, usually involving a lot of mathematics...
When looking at the literature, I was expecting to see methods similar to self-training (from semi-supervised learning), where labels are adjusted gradually according to the classifier margins.
I don't think these is what practitioners from the area do, and I was unable to navigate the mathematics or to find a survey on PU learning.
Could someone from the area perhaps clarify what said practitioners do? Why can they not just use a binary classifier where the negative class=unlabeled? Can negative labels exist among the unlabeled data? What is the goal and what metrics exist to evaluate said goal?