.png){kind=icon}
返回
Incomplete Multi-view Clustering via Virtual-label Guided Matrix Factorization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.118408.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-view clustering methods have achieved appealing performance by exploring the data from multiple sources. It often assumes that the multiple-view data are complete. However, such rigorous assumption does not be satisfied in many real-world applications, in which only some views of data are available. The existence of the incomplete-view data would disable the current multi-view clustering algorithms. To address this challenge, in this article, we present a novel virtual-label guided matrix factorization (VLMF) to facilitate the incomplete multi-view clustering. Specifically, in VLMF, we first employ the graph regularization of each view to mine the geometrical structure of data. Then, we utilize the virtual label to guide matrix factorization. Furthermore, the clustering process and the consensus latent representation learning of data are integrated into a joint framework. Experimental results on several incomplete multi-view datasets illustrate the validity of the proposed method.
Keyword:
incomplete views
virtual label
consensus latent representation
matrix factorization
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance共识锚引导的无参数多视图子空间快速聚类
PREAI
分享收藏分享收藏Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition用于目标识别的判别式Fisher嵌入字典学习算法
PREAI
分享收藏Learning disentangled user representation with multi-view information fusion on social networks.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Clinical profiles, outcomes and risk factors among type 2 diabetic inpatients with diabetic ketoacidosis and hyperglycemic hyperosmolar state: a hospital-based analysis over a 6-year period糖尿病酮症酸中毒和高血糖高渗状态的2型糖尿病住院患者的临床特征，结局和危险因素: 基于医院的6年分析
OAAI
OAAI分享收藏分享收藏
查看更多内容