Learning with limited and noisy tagging

Yingming Li, Zhongang Qi, Zhongfei Zhang, Mingyuan Yang
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引用次数: 7

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.
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使用有限和嘈杂的标签进行学习
随着社交网络的快速发展,标签已经成为推动社交网络快速发展的重要手段。鲁棒标记方法必须能够满足两个具有挑战性的要求:有限标记的训练样本和有噪声标记的训练样本。在本文中,我们研究了这个具有挑战性的问题,并提出了一个判别模型,称为SpSVM-MC,该模型通过半参数正则化来利用标记和未标记的数据,并利用多标签约束进行优化。虽然SpSVM-MC是一种使用有限标记和噪声标记进行学习的通用方法,但在评估中,我们关注的是在基准数据集上使用有限标记训练样本进行噪声图像标记的具体应用。理论分析和广泛的评价与最新的文献比较表明,SpSVM-MC具有优异的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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