  Multi-label learning studies the problem where an instance is associated with
a set of labels. By treating single-label learning problem as one task, the
multi-label learning problem can be casted as solving multiple related tasks
simultaneously. In this paper, we propose a novel Multi-task Gradient Descent
(MGD) algorithm to solve a group of related tasks simultaneously. In the
proposed algorithm, each task minimizes its individual cost function using
reformative gradient descent, where the relations among the tasks are
facilitated through effectively transferring model parameter values across
multiple tasks. Theoretical analysis shows that the proposed algorithm is
convergent with a proper transfer mechanism. Compared with the existing
approaches, MGD is easy to implement, has less requirement on the training
model, can achieve seamless asymmetric transformation such that negative
transfer is mitigated, and can benefit from parallel computing when the number
of tasks is large. The competitive experimental results on multi-label learning
datasets validate the effectiveness of the proposed algorithm.
