Semisupervised multitask learning

Qiuhua Liu, Xuejun Liao, Hui Li Carin, Jason R. Stack, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Context plays an important role when performing classification, and in this paper we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks (multiple distinct data collections). This is referred to as multi-task learning (MTL), and is implemented here in a statistical manner, using a simplified form of the Dirichlet process. In addition, when performing many classification tasks one has simultaneous access to all unlabeled data that must be classified, and therefore there is an opportunity to place the classification of any one feature vector within the context of all unlabeled feature vectors; this is referred to as semi-supervised learning. In this paper we integrate MTL and semi-supervised learning into a single framework, thereby exploiting two forms of contextual information. Example results are presented on a "toy" example, to demonstrate the concept, and the algorithm is also applied to three real data sets. © 2009 IEEE.
Original languageEnglish (US)
Pages (from-to)1074-1086
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume31
Issue number6
DOIs
StatePublished - May 20 2009
Externally publishedYes

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