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Multi-Task Learning with Group-Specific Feature Space Sharing
|Title||Multi-Task Learning with Group-Specific Feature Space Sharing|
|Publication Type||Conference Paper|
|Year of Publication||2015|
|Authors||Yousefi N, Georgiopoulos M, Anagnostopoulos GC|
|Conference Name||Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015|
|Publisher||Springer International Publishing|
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. (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.
Acceptance rate 23.4% (89/380).