Self-Supervised deep correlational multi-view clustering
In conventional unsupervised multi-view clustering (MVC), learning of representations from
heterogeneous multiview data and its subsequent clustering are often separately optimized …
heterogeneous multiview data and its subsequent clustering are often separately optimized …
Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
Multi-view clustering (MVC) aims at exploring category structures among multi-view data in
self-supervised manners. Multiple views provide more information than single views and …
self-supervised manners. Multiple views provide more information than single views and …
One-stage multi-view clustering with hierarchical attributes extraction
Multi-view clustering (MVC) has received significant attention, and obtained praiseworthy
performance improvement in comparison with signal-view clustering, since it can effectively …
performance improvement in comparison with signal-view clustering, since it can effectively …
Deep Multiview Clustering by Pseudo-Label Guided Contrastive Learning and Dual Correlation Learning
Deep multiview clustering (MVC) is to learn and utilize the rich relations across different
views to enhance the clustering performance under a human-designed deep network …
views to enhance the clustering performance under a human-designed deep network …
Structure-guided feature and cluster contrastive learning for multi-view clustering
Z Shu, B Li, C Mao, S Gao, Z Yu - Neurocomputing, 2024 - Elsevier
Multi-view clustering (MVC) technology performs unsupervised clustering on data collected
from multiple sources, and has received intense attention in recent years. However, most …
from multiple sources, and has received intense attention in recent years. However, most …
SC2-Net: Self-supervised learning for multi-view complementarity representation and consistency fusion network
L Huang, X Fan, T Xia, Y Li, Y Ding - Neurocomputing, 2023 - Elsevier
Multi-view clustering (MVC) seeks to improve the original single-view clustering by exploring
the complementarity and consistency contained in multi-view data. While most subspace …
the complementarity and consistency contained in multi-view data. While most subspace …
Joint contrastive triple-learning for deep multi-view clustering
Deep multi-view clustering (MVC) is to mine and employ the complex relationships among
views to learn the compact data clusters with deep neural networks in an unsupervised …
views to learn the compact data clusters with deep neural networks in an unsupervised …
Self-supervised discriminative feature learning for deep multi-view clustering
Multi-view clustering is an important research topic due to its capability to utilize
complementary information from multiple views. However, there are few methods to consider …
complementary information from multiple views. However, there are few methods to consider …
Learning consensus representations in multi-latent spaces for multi-view clustering
Multi-view clustering integrates features from different views to perform clustering. This
problem has attracted increasing attention in recent years because multi-view data has …
problem has attracted increasing attention in recent years because multi-view data has …
Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a
hot research spot, which aims to mine the potential relationships between different views …
hot research spot, which aims to mine the potential relationships between different views …