2012 InductiveMultiTaskLearningwithM

• (Zhang & Huan, 2012) ⇒ Jintao Zhang, and Jun Huan. (2012). “Inductive Multi-task Learning with Multiple View Data.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339617

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• http://scholar.google.com/scholar?q=%222012%22+Inductive+Multi-task+Learning+with+Multiple+View+Data
• http://dl.acm.org/citation.cfm?id=2339530.2339617&preflayout=flat#citedby

Quotes

Author Keywords

• Co-regularization; inductive multi-task learning; learning; miscellaneous; missing view; multi-view learning; task relationship learning

Abstract

In many real-world applications, it is becoming common to have data extracted from multiple diverse sources, known as " multi-view " data. Multi-view learning (MVL) has been widely studied in many applications, but existing MVL methods learn a single task individually. In this paper, we study a new direction of multi-view learning where there are multiple related tasks with multi-view data (i.e. multi-view multi-task learning, or MVMT Learning). In our MVMT learning methods, we learn a linear mapping for each view in each task. In a single task, we use co-regularization to obtain functions that are in-agreement with each other on the unlabeled samples and achieve low classification errors on the labeled samples simultaneously. Cross different tasks, additional regularization functions are utilized to ensure the functions that we learn in each view are similar. We also developed two extensions of the MVMT learning algorithm. One extension handles missing views and the other handles non-uniformly related tasks. Experimental studies on three real-world data sets demonstrate that our MVMT methods significantly outperform the existing state-of-the-art methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 InductiveMultiTaskLearningwithM	Jun Huan Jintao Zhang			Inductive Multi-task Learning with Multiple View Data				10.1145/2339530.2339617		2012