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 …

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 …

Here, we provide the proofs for Propositions 2 and 3; additional details on the proposed new
instances of DeepMVC; the datasets used for evaluation; the hyperparameters used by …

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 …

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 …

Deep multi-view clustering has been widely studied. However, since it is an unsupervised
task, where no labels are used to guide the training, it is still unreliable especially when …

Multi-view clustering (MVC) technology performs unsupervised clustering on data collected
from multiple sources, and has received intense attention in recent years. However, most …

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 …

Multi-view clustering has attracted growing attention owing to its powerful capacity of multi-
source information integration. Although numerous advanced methods have been proposed …

Multi-view clustering is a long-standing important research topic, however, remains
challenging when handling high-dimensional data and simultaneously exploring the …