报告主题：超图学习：从代价敏感角度
报告人：高跃 副教授 （清华大学）
报告时间：2017年 11月23日（周四）9:00
报告地点：校本部G507
邀请人：应时辉
报告摘要：Hypergraph is a general graph structure and has been widely applied in data classification, image segmentation and retrieval due to its superior performance on high-order correlation modelling. In recent years, extensive research efforts have been dedicated to hypergraph based learning methods. In this presentation, we will first introduce the hypergraph construction methods, considering both single modality and multi-modality scenarios. After that, we will present the learning methods on hypergraph structure, from traditional transductive learning to hypergraph structure learning, including the information about vertex, hyperedge and multi-hypergraphs. We mainly introduce a cost-sensitive hypergraph learning framework to handle the cost issue in classification tasks. Finally, we will introduce the applications of hypergraph structure learning in medical image analysis.
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