Results 1 to 10 of about 18,214 (96)

Soft label collaborative view consistency enhancement with application to incomplete multi-view clustering. [PDF]

PLoS ONE
Incomplete multi-view clustering (IMVC) is an unsupervised technique for clustering multi-view data when some view information is absent. However, most existing IMVC methods usually suffer from several significant challenges: (1) Inaccurate imputation or
Jie Zhang, Jiali Tang
doaj   +2 more sources

Late Fusion Incomplete Multi-View Clustering. [PDF]

IEEE Trans Pattern Anal Mach Intell, 2019
Liu X, Zhu X, Li M, Wang L, Tang C, Yin J, Shen D, Wang H, Gao W.   +8 more
europepmc   +2 more sources

Kernel-Induced Incomplete Multi-view Clustering

Jisuanji kexue yu tansuo, 2021
With the development of technology, data often have multiple forms which come from multiple sources. The multi-view clustering algorithm aims to use the complementary information existing in different sources for clustering.
ZHANG Wei, DENG Zhaohong, WANG Shitong
doaj   +1 more source

Effective Incomplete Multi-View Clustering via Low-Rank Graph Tensor Completion

Mathematics, 2023
In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view ...
Jinshi Yu, Qi Duan, Haonan Huang, Shude He, Tao Zou   +4 more
doaj   +1 more source

Auto-Weighted Incomplete Multi-View Clustering

IEEE Access, 2020
Nowadays, multi-view clustering has attracted more and more attention, which provides a way to partition multi-view data into their corresponding clusters. Previous studies assume that each data instance appears in all views.
Wanyu Deng, Lixia Liu, Jianqiang Li, Yijun Lin   +3 more
doaj   +1 more source

Anchor Pseudo-Supervise Large-Scale Incomplete Multi-View Clustering

IEEE Access, 2023
In real life, only partial information of samples is available everywhere, this makes Incomplete multi-view clustering (IMVC) becomes a significant research topic to handle data loss situations.
Songbai Zhu, Jian Dai, Guolai Yang, Zhenwen Ren   +3 more
doaj   +1 more source

Multi-View Spectral Clustering With Incomplete Graphs

IEEE Access, 2020
Traditional multi-view learning usually assumes each instance appears in all views. However, in real-world applications, it is not an uncommon case that a number of instances suffer from some view samples missing.
Wenzhang Zhuge, Tingjin Luo, Hong Tao, Chenping Hou, Dongyun Yi   +4 more
doaj   +1 more source

Self‐supervised image clustering from multiple incomplete views via constrastive complementary generation

IET Computer Vision, 2023
Incomplete Multi‐View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: (1) It is ...
Jiatai Wang, Zhiwei Xu, Xuewen Yang, Dongjin Guo, Limin Liu   +4 more
doaj   +1 more source

Complete/incomplete multi‐view subspace clustering via soft block‐diagonal‐induced regulariser

IET Computer Vision, 2021
This study proposes a novel multi‐view soft block diagonal representation framework for clustering complete and incomplete multi‐view data. First, given that the multi‐view self‐representation model offers better performance in exploring the intrinsic ...
Yongli Hu, Cuicui Luo, Boyue Wang, Junbin Gao, Yanfeng Sun, Baocai Yin   +5 more
doaj   +1 more source

Incomplete Multi-View Clustering via Auto-Weighted Fusion in Partition Space

Tsinghua Science and Technology, 2023
As a class of effective methods for incomplete multi-view clustering, graph-based algorithms have recently drawn wide attention. However, most of them could use further improvement regarding the following aspects.
Dongxue Xia, Yan Yang, Shuhong Yang
doaj   +1 more source