Multi-view spectral clustering, which exploits the complementary information among graphs
of diverse views to obtain superior clustering results, has attracted intensive attention …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Multi-view clustering divides similar objects into the same class through using the fused
multiview information. Most multi-view clustering methods obtain clustering result by only …

Non-negative matrix factorization (NMF) has attracted sustaining attention in multi-view
clustering, because of its ability of processing high-dimensional data. In order to learn the …

Clustering ensemble integrates multiple base clustering results to obtain a consensus result
and thus improves the stability and robustness of the single clustering method. Since it is …

Subspace techniques have gained much attention for their remarkable efficiency in
representing high-dimensional data, in which sparse subspace clustering (SSC) and low …

Multi-view clustering based on graph learning has attracted extensive attention due to its
simplicity and efficiency in recent years. However, there are still some issues in most of the …

Consensus clustering, which learns a consensus clustering result from multiple weak base
results, has been widely studied. However, conventional consensus clustering methods only …

The clustering ensemble has emerged as an important extension of the classical clustering
problem. It provides an elegant framework to integrate multiple weak base clusterings to …

Generally, the existing graph-based multi-view clustering models consists of two steps:(1)
graph construction;(2) eigen-decomposition on the graph Laplacian matrix to compute a …