NDLI: Multi-task Semi-supervised Semantic Feature Learning for Classification

Content Provider	IEEE Xplore Digital Library
Author	Changying Du Fuzhen Zhuang Qing He Zhongzhi Shi
Copyright Year	2012
Abstract	Multi-task learning has proven to be useful to boost the learning of multiple related but different tasks. Meanwhile, latent semantic models such as LSA and LDA are popular and effective methods to extract discriminative semantic features of high dimensional dyadic data. In this paper, we present a method to combine these two techniques together by introducing a new matrix tri-factorization based formulation for semi-supervised latent semantic learning, which can incorporate labeled information into traditional unsupervised learning of latent semantics. Our inspiration for multi-task semantic feature learning comes from two facts, i.e., 1) multiple tasks generally share a set of common latent semantics, and 2) a semantic usually has a stable indication of categories no matter which task it is from. Thus to make multiple tasks learn from each other we wish to share the associations between categories and those common semantics among tasks. Along this line, we propose a novel joint Nonnegative matrix tri-factorization framework with the aforesaid associations shared among tasks in the form of a semantic-category relation matrix. Our new formulation for multi-task learning can simultaneously learn (1) discriminative semantic features of each task, (2) predictive structure and categories of unlabeled data in each task, (3) common semantics shared among tasks and specific semantics exclusive to each task. We give alternating iterative algorithm to optimize our objective and theoretically show its convergence. Finally extensive experiments on text data along with the comparison with various baselines and three state-of-the-art multi-task learning algorithms demonstrate the effectiveness of our method.
Starting Page	191
Ending Page	200
File Size	360056
Page Count	10
File Format	PDF
ISBN	9781467346498
ISSN	15504786
DOI	10.1109/ICDM.2012.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Optimization Feature extraction Data mining Data models Joints Iterative methods text classification multi-task learning semantic feature learning semi-supervised learning joint nonnegative matrix tri-factorization
Content Type	Text
Resource Type	Article

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