CAREER: Large-Scale Multi-label Learning

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Important applications in science and business depend on automatic classification of large volumes of items (examples) into predefined classes. Multi-label learning refers to the classification problem where each example can be assigned to multiple class labels simultaneously. The classification problem is germane to many different domains, such as natural language processing, computer vision, human computer interaction, bioinformatics, health care, and physiology. Existing machine learning technologies are unsuitable for large-scale multi-label learning because they are unable to handle rare class classification problems and poorly distinguish classes with similar input patterns. To overcome the limitation of the existing approaches, the project is developing a relation propagation framework for multi-label learning that explicitly exploits the similarity of examples and the correlation among classes simultaneously. In particular, this project includes research to (1) develop efficient optimization algorithms for the proposed relation propagation framework; (2) develop effective algorithms for learning the similarity of examples and the correlation among classes; (3) develop effective active learning algorithms for multi-label learning; and (4) evaluate the proposed framework for multi-label learning through three real world applications: text categorization, image annotation, and prediction of gene expression patterns.The project will advance the state of the art of techniques for large-scale multi-label learning through the development of relation propagation framework, which in return will have a significant impact on a wide range of applications. The research results will also enhance the current machine learning curricula, involve students in interdisciplinary research and improve the education of the information technology workforce. The project Web site (http://www.cse.msu.edu/~rongjin/) will be used for research results dissemination.