Learning with limited and noisy tagging

Authors:
Yingming Li;Zhongang Qi;Zhongfei (Mark) Zhang;Ming Yang
Affiliations:
Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China
Venue:
Proceedings of the 21st ACM international conference on Multimedia
Year:
2013

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Abstract

With the rapid development of social networks, tagging has become an important means responsible for such rapid development. A robust tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called SpSVM-MC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label constraints into the optimization. While SpSVM-MC is a general method for learning with limited and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Theoretical analysis and extensive evaluations in comparison with state-of-the-art literature demonstrate that SpSVM-MC outstands with a superior performance.