Learning from High-Dimensional Data in Multitask/Multilabel Classification

Real-world data sets are highly complicated. They can contain a lot of features, and may involve multiple learning tasks with intrinsically or explicitly represented task relationships. In this paper, we briefly discuss several recent approaches that can be used in these scenarios. The algorithms presented are flexible in capturing the task relationships, computationally efficient with good scalability, and have better empirical performance than the existing approaches.