  When faced with learning a set of inter-related tasks from a limited amount
of usable data, learning each task independently may lead to poor
generalization performance. Multi-Task Learning (MTL) exploits the latent
relations between tasks and overcomes data scarcity limitations by co-learning
all these tasks simultaneously to offer improved performance. We propose a
novel Multi-Task Multiple Kernel Learning framework based on Support Vector
Machines for binary classification tasks. By considering pair-wise task
affinity in terms of similarity between a pair's respective feature spaces, the
new framework, compared to other similar MTL approaches, offers a high degree
of flexibility in determining how similar feature spaces should be, as well as
which pairs of tasks should share a common feature space in order to benefit
overall performance. The associated optimization problem is solved via a block
coordinate descent, which employs a consensus-form Alternating Direction Method
of Multipliers algorithm to optimize the Multiple Kernel Learning weights and,
hence, to determine task affinities. Empirical evaluation on seven data sets
exhibits a statistically significant improvement of our framework's results
compared to the ones of several other Clustered Multi-Task Learning methods.
