Proceedings

Multi-View Learning with Limited and Noisy Tagging

Yingming Li, Ming Yang, Zenglin Xu, Zhongfei Zhang

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1718-1723.

Abstract

Multi-view tagging has become increasingly popular in the applications where data representations by multiple views exist. A robust multi-view 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 MSMC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label space consistency into the optimization. While MSMC is a general method for learning with multi-view, 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. Extensive evaluations in comparison with state-of-the-art literature demonstrate that MSMC outstands with a superior performance.

Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei Zhang. 2016. Multi-View Learning with Limited and Noisy Tagging. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 1718–1723. https://ijcai.org/proceedings/2016/246
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