Multi-label learning has attracted significant attention from both academic
and industry field in recent decades. Although existing multi-label learning
algorithms achieved good performance in various tasks, they implicitly assume
the size of target label space is not huge, which can be restrictive for
real-world scenarios. Moreover, it is infeasible to directly adapt them to
extremely large label space because of the compute and memory overhead.
Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and
many effective approaches are proposed. To fully understand XML, we conduct a
survey study in this paper. We first clarify a formal definition for XML from
the perspective of supervised learning. Then, based on different model
architectures and challenges of the problem, we provide a thorough discussion
of the advantages and disadvantages of each category of methods. For the
benefit of conducting empirical studies, we collect abundant resources
regarding XML, including code implementations, and useful tools. Lastly, we
propose possible research directions in XML, such as new evaluation metrics,
the tail label problem, and weakly supervised XML.Comment: A preliminary versio