.png){kind=icon}
返回
Clustering-Aware Graph Construction: A Joint Learning Perspective
.png){kind=icon}
OA
AI
DOI：10.1109/TSIPN.2020.2988572.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Adaptive graph learning
Clustering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.9
论文数:
734
被引数:
1.9K
机构
引用论文
.png){kind=icon}
分享收藏Double Selection Based Semi-Supervised Clustering Ensemble for Tumor Clustering from Gene Expression Profiles基于双重选择的半监督聚类集成用于基因表达谱的肿瘤聚类
PREAI
分享收藏The changing epidemiology of paediatric brain tumours: a review from the Hospital for Sick Children
PREAI
分享收藏
查看更多内容