Clustering-Aware Graph Construction: A Joint Learning Perspective
计算机科学
聚类分析
人工智能
图形
机器学习
作者
Yuheng Jia,Hui Liu,Junhui Hou,Sam Kwong
出处
期刊:IEEE transactions on signal and information processing over networks [Institute of Electrical and Electronics Engineers] 日期:2020-04-20卷期号:6: 357-370被引量:9
Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed method is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem,which is optimized by an alternative iteration method with the convergence of the value of the objective function guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics.