Multitask Learning without Label Correspondences.
Novi Quadrianto, Alex Smola, Tiberio Caetano, S V N Vishwanathan and James Petterson
In: NIPS, 6-11 Dec 2010, Vancouver, Canada.
We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available. This is in contrast with existing methods which either assume that the label sets shared by different tasks are the same or that there exists a label mapping oracle. Our method directly maximizes the mutual information among the labels, and we show that the resulting objective function can be efﬁciently optimized using existing algorithms. Our proposed approach has a direct application for data integration with different label spaces, such as integrating Yahoo! and DMOZ web directories.