Multi-view clustering (MVC) holds a significant role in domains like machine learning, data
mining, and pattern recognition. Despite the development of numerous new MVC …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …

Multi-view bipartite graph clustering methods select a few representative anchors and then
establish a connection with original samples to generate the bipartite graphs for clustering …

Historical Quality-of-Service (QoS) data describing existing user-service invocations are vital
to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) …

The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …

Abstract Recently, Multi-view Representation Learning (MRL) has drawn immense
attentions in the analysis of multi-source data and ubiquitously employed across different …

Multi-view clustering aims to partition multi-view data into different categories by optimally
exploring the consistency and complementary information from multiple sources. However …

Multi-view clustering is widely used to improve clustering performance. Recently, the
subspace clustering tensor learning method based on Markov chain is a crucial branch of …

Abstract Graph Convolutional Network (GCN) has drawn widespread attention in data
mining on graphs due to its outstanding performance and rigor theoretical guarantee …