讲座题目：A Restarted Large-Scale Spectral Clustering with Self-Guiding and Block Diagonal Representation

主 讲 人：吴钢 教授（中国矿业大学）

讲座时间：2023年11月24日（周五）14:00 - 14:45

讲座地点：钱伟长楼201会议室

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2023年11月22日

讲座内容简介：

Spectral clustering is one of the most popular unsupervised machine learning methods. Constructing similarity matrix is crucial to this type of method. In most existing works, the similarity matrix is computed once for all or is updated alternatively. However, the former is difficult to reflect comprehensive relationships among data points, and the latter is time-consuming and is even infeasible for large-scale problems. In this work, we propose a restarted clustering framework with self-guiding and block diagonal representation. An advantage of the framework is that some useful clustering information obtained from previous cycles could be preserved as much as possible. To the best of our knowledge, this is the first work that applies this strategy to spectral clustering. The key difference is that we reclassify the samples in each cycle of our method, while they are classified only once in existing methods. To further release the overhead, we introduce a block diagonal representation with Nystr\"{o}m approximation for constructing the similarity matrix. Theoretical results are established to show the rationality of inexact computations in spectral clustering.

Comprehensive experiments are performed on some benchmark databases, which show the superiority of our proposed algorithms over many state-of-the-art algorithms for large-scale problems. Specifically, our framework has a potential boost for clustering algorithms and works well even using an initial guess chosen randomly.

主讲人简介：

吴钢，博士、中国矿业大学数学学院教授、博士生导师；江苏省“333工程”中青年科学技术带头人，江苏省“青蓝工程”中青年学术带头人，江苏省计算数学学会副理事长。主要研究方向：数值代数、机器学习与数据挖掘、大规模科学与工程计算等。先后主持国家自然科学基金项目、江苏省省自然科学基金项目多项，在国际知名杂志，如：SIAM Journal on Numerical Analysis, SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, IMA Journal of Numerical Analysis, Pattern Recognition, Machine Learning等期刊发表学术论文多篇。