Semi-Supervised Multitask Learning

A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M par- tially labeled data manifolds, are learned jointly under the constraint of a soft- sharing prior imposed over the parameters of the classifiers. The unlabeled data are utilized by basing classifier learning on neighborhoods, induced by a Markov random walk over a graph representation of each manifold. Experimental results on real data sets demonstrate that semi-supervised MTL yields significant im- provements in generalization performance over either semi-supervised single-task learning (STL) or supervised MTL.

Authors

• Qiuhua Liu
• Xuejun Liao
• Lawrence Carin

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