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 …
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 …
On the effects of self-supervision and contrastive alignment in deep multi-view clustering
Self-supervised learning is a central component in recent approaches to deep multi-view
clustering (MVC). However, we find large variations in the development of self-supervision …
clustering (MVC). However, we find large variations in the development of self-supervision …
On the Role of Self-supervision in Deep Multi-view Clustering
Self-supervised learning is a central component in many recent approaches to deep multi-
view clustering (MVC). However, we find large variations in the motivation and design of self …
view clustering (MVC). However, we find large variations in the motivation and design of self …
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 …
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 …
Deep multi-view semi-supervised clustering with sample pairwise constraints
Multi-view clustering has attracted much attention thanks to the capacity of multi-source
information integration. Although numerous advanced methods have been proposed in past …
information integration. Although numerous advanced methods have been proposed in past …
Gcfagg: Global and cross-view feature aggregation for multi-view clustering
Multi-view clustering can partition data samples into their categories by learning a
consensus representation in unsupervised way and has received more and more attention …
consensus representation in unsupervised way and has received more and more attention …
Multi-VAE: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering
Multi-view clustering, a long-standing and important research problem, focuses on mining
complementary information from diverse views. However, existing works often fuse multiple …
complementary information from diverse views. However, existing works often fuse multiple …
Deep multiview clustering by contrasting cluster assignments
Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by
categorizing data samples into clusters. Deep learning-based methods exhibit strong feature …
categorizing data samples into clusters. Deep learning-based methods exhibit strong feature …