PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multitask Learning without Label Correspondences
Novi Quadrianto, A.J. Smola, Tiberio Caetano, S.V.N. Vishwanathan and James Petterson
In: Advances in Neural Information Processing Systems (NIPS)(2011).


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:7336
Deposited By:Wray Buntine
Deposited On:17 March 2011