Learning task structure via sparsity grouped multitask learning
This addresses a bottleneck in multitask learning for high-dimensional problems where grouping information is typically unknown, offering an incremental improvement over existing sparse mapping techniques.
The paper tackles the problem of recovering hidden group structures in sparsity patterns across multiple tasks, proposing a joint optimization method that accurately recovers both groups and sparsity patterns in task parameters.
Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However, in practice this technique is used limitedly since such grouping information is usually hidden. In this paper, our goal is to recover the group structure on the sparsity patterns and leverage that information in the sparse learning. Toward this, we formulate a joint optimization problem in the task parameter and the group membership, by constructing an appropriate regularizer to encourage sparse learning as well as correct recovery of task groups. We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.