题 目：Clustering by Unified Principal Component Analysis and Fuzzy c-means with Sparsity Constraint

报告人：王继奎 副教授

时 间：2020年10月17日08:10-12:15

地 点：兰州财经大学段家滩校区艺术楼504室（信工学院会议室）

内 容：For clustering high-dimensional data, most of the state-of-the-art algorithms often extract principal component beforehand, and then conduct a concrete clustering method. However, the two-stage strategy may deviate from assignments by directly optimizing the unified objective function. Different from the traditional methods, we propose a novel method referred to as clustering by unified principal component analysis and fuzzy c-means(UPF) for clustering high-dimensional data. Our model can explore underlying clustering structure in low-dimensional space and finish clustering simultaneously. In particular, we impose a L0-norm constraint on the membership matrix to make the matrix more sparse. To solve the model, we propose an effective iterative optimization algorithm. Extensive experiments on several benchmark data sets in comparison with two-stage algorithms are conducted to validate effectiveness of the proposed method. The experiments results demonstrate that the performance of our proposed method is superiority.

报告人简介：

王继奎，博士，兰州财经大学副教授。博士毕业于中国科学院大学，曾在大数据系统计算技术国家工程实验室从事博士后研究工作，主要研究兴趣为机器学习理论和方法及其应用等。近五年共发表SCI/EI/CSCD论文10篇; 主持、参与省部级及以上项目3项；获得兰州市科学技术进步2等奖1项。