Abstract
In multi-task learning (MTL), tasks are learned
jointly so that information among related tasks is
shared and utilized to help improve generalization
for each individual task. A major challenge in MTL
is how to selectively choose what to share among
tasks. Ideally, only related tasks should share information with each other. In this paper, we propose a new MTL method that can adaptively group
correlated tasks into clusters and share information among the correlated tasks only. Our method
is based on the assumption that each task parameter is a linear combination of other tasks’ and
the coefficients of the linear combination are active
only if there is relatedness between the two tasks.
Through introducing trace Lasso penalty on these
coefficients, our method is able to adaptively select
the subset of coefficients with respect to the tasks
that are correlated to the task. Our model frees
the process of determining task clustering structure as used in the literature. Efficient optimization method based on alternating direction method
of multipliers (ADMM) is developed to solve the
problem. Experimental results on both synthetic
and real-world datasets demonstrate the effectiveness of our method in terms of clustering related
tasks and generalization performance