报告题目：Global Understanding via Local Extraction for Data Clustering and Visualization
报告人：张振跃 教授单位：浙江大学\深圳北理莫斯科大学
报告时间：2026年11月14日（周六）上午 10:00-11:30
报告地点：数学学院A321
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报告人及报告内容简介：
张振跃，深圳北理莫斯科大学特聘教授，浙江大学二级教授，博士生导师。浙江大学心平教学杰出贡献奖获得者。主要从事数值代数、科学计算、机器学习和大数据分析等研究领域的模型与算法的理论分析与计算。先后发表近百篇学术论文。许多高质量研究论文发表在国际著名学术刊物，如：SIAM Review、Patterns、SIAM J. Scientific Computing、SIAM J. Matrix Analysis and Application、SIAM J Numerical Analysis、The Journal of American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence、Journal of Machine Learning Research, Patten Recognition, 以及NIPS、CVPR等学术会议刊物。张振跃教授是第一位在SIAM Review上发表研究论文的国内大陆学者，持续多年高被引作者。其关于非线性降维算法的工作，多年来一直列SIAM J. Scientific Computing 10年高引用率第4、5位；两个关于流形学习算法被国际机器学习中应用广泛的Scikit-Learn 中收录；发表在Patterns上工作，在无数据分布或结构假设下，解决了无监督数据高准确度聚类问题。
Abstract: Retrieving latent class patterns from complex data is challenging. This talk focuses on the problem of retrieving latent classes from local connections of raw data without any assumptions regarding data structures or distributions. We propose a framework called GULE (global understanding via local extraction) to address this challenge through both local extraction of class consistency and global propagation of the identified consistency. This paper provides a series of theoretical analyses to show why the GULE algorithm can retrieve latent classes with high accuracy. GULE can also serve as a tool for data visualization to preserve class topology structures. Comprehensive testing demonstrates that GULE provides precise clustering and highly reliable visualizations, potentially offering insights into diverse applications, including biology and medicine.