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2008

Instance-level Semisupervised Multiple Instance Learning

13 years 6 months ago
Instance-level Semisupervised Multiple Instance Learning
Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-based image retrieval and text categorization can be viewed as MIL problems. In this paper, we propose a new graph-based semi-supervised learning approach for multiple instance learning. By defining an instance-level graph on the data, we first propose a new approach to construct an optimization framework for multiple instance semi-supervised learning, and derive an efficient way to overcome the non-convexity of MIL. We empirically show that our method outperforms state-of-the-art MIL algorithms on several real-world data sets.
Yangqing Jia, Changshui Zhang
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Yangqing Jia, Changshui Zhang
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