报告人：吴钢 教授（中国矿业大学）
报告时间：2025年9月24日，20:00
腾讯会议：503293-759 会议密码：55555
报告摘要：
Multi-view clustering is a powerful approach for discovering underlying structures hidden behind diverse views of datasets. Most existing multi-view spectral clustering methods use fixed similarity matrices or alternately updated ones. However, the former often fall short in adaptively capturing relationships among different views, while the latter are often time-consuming and even impractical for large-scale datasets. To the best of our knowledge, there are no multi-view spectral clustering methods can both construct multiview similarity matrices inexpensively and preserve the valuable clustering insights from previous cycles at the same time. To fill in this gap, we present a Sum-Ratio Multi-view Ncut model that share a common representation embedding for multi-view data. Based on this model, we propose a restarted multi-view multiple kernel clustering framework with self-guiding. To release the overhead, we use similarity matrices with strict block diagonal representation, and present an efficient multiple kernel selection technique. Comprehensive experiments on benchmark multi-view datasets demonstrate that, even using randomly generated initial guesses, the restarted algorithms can improve the clustering performances by 5–10 times for some popular multi-view clustering methods. Specifically, our framework offers a potential boosting effect for most of the state-of-the-art multi-view clustering algorithms at very little cost, especially for those with poor performances.
报告人简介：
吴钢，中国矿业大学教授，博士，博士生导师，现任江苏省计算数学学会副理事长，江苏省“青蓝工程”中青年学术带头人，江苏省“333工程”中青年科学技术带头人，先后主持国家自然科学基金项目4项、江苏省自然科学基金面上项目2项，徐州市重点研发计划1项，江苏省教育厅自然科学基金项目1项。 在SIAM Journal on Numerical Analysis, SIAM Journal on Scientific Computing, SIAM Journal on Matrix Analysis and Applications, IMA Journal of Numerical Analysis, IEEE Transactions on Knowledge and Data Engineering, Pattern Recognition, Machine Learning, ACM Transactions on Information Systems等著名期刊发表学术论文多篇。
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