.png){kind=icon}
返回
A survey on representation learning for multi-view data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2024.106842.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 has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering into consideration. We give a novel survey for sorting out the existing algorithms of multi-view clustering in this work, which can be classified into two different categories, i.e., non-self supervised and self-supervised multi-view clustering. We first review the representative approaches based on the non-self supervised multi-view clustering, which consist of methods based on non- representation learning and representation learning. Furthermore, the methods built on non-representation learning contain works based on matrix factorization, kernel and other non-representation learning. Methods based on representation learning consist of multi-view graph clustering, deep representation learning and multi- view subspace clustering. For the methods based on self-supervised multi-view clustering, we divide them into contrastive methods and generative methods. Overall, this survey attempts to give an insightful overview regarding the developments in the multi-view clustering field.
Keyword:
Self-supervised multi-view clustering
Non-self supervised multi-view clustering
Contrastive methods
Generative methods
Non-representation learning-based method
Representation learning-based method
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.0W
机构
引用论文
Osteoblast-derived WISP-1 increases VCAM-1 expression and enhances prostate cancer metastasis by down-regulating miR-126.png)
Oncotarget
OAAI
OncotargetIF0
OAAI分享收藏分享收藏Local2Global: Unsupervised multi-view deep graph representation learning with Nearest Neighbor ConstraintLocal2Global: 具有最近邻约束的无监督多视图深度图表示学习
PREAI
分享收藏Structured subspace learning-induced symmetric nonnegative matrix factorizationSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
查看更多内容