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 …
Multiview subspace clustering via low-rank symmetric affinity graph
Multiview subspace clustering (MVSC) has been used to explore the internal structure of
multiview datasets by revealing unique information from different views. Most existing …
multiview datasets by revealing unique information from different views. Most existing …
A multi-view clustering algorithm based on deep semi-NMF
Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …
Spectral embedding fusion for incomplete multiview clustering
Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete
multiview data by partitioning data samples into clusters. Several graph-based methods …
multiview data by partitioning data samples into clusters. Several graph-based methods …
Multiview clustering by consensus spectral rotation fusion
Multiview clustering (MVC) aims to partition data into different groups by taking full
advantage of the complementary information from multiple views. Most existing MVC …
advantage of the complementary information from multiple views. Most existing MVC …
Multi-view subspace clustering via adaptive graph learning and late fusion alignment
Multi-view subspace clustering has attracted great attention due to its ability to explore data
structure by utilizing complementary information from different views. Most of existing …
structure by utilizing complementary information from different views. Most of existing …
A semi-supervised label-driven auto-weighted strategy for multi-view data classification
Distinguishing the importance of views plays a key role in multi-view learning as each view
often contributes differently to a specific task. However, existing strategies generally attach …
often contributes differently to a specific task. However, existing strategies generally attach …
Multi-view MERA subspace clustering
Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in
the self-representation tensor. Current tensor decompositions for MSC suffer from highly …
the self-representation tensor. Current tensor decompositions for MSC suffer from highly …
Clean affinity matrix learning with rank equality constraint for multi-view subspace clustering
J Zhao, GF Lu - Pattern Recognition, 2023 - Elsevier
The existing multi-view subspace clustering (MVSC) algorithm still has certain limitations.
First, the affinity matrix obtained by them is not clean and robust enough since the original …
First, the affinity matrix obtained by them is not clean and robust enough since the original …
Scalable one-stage multi-view subspace clustering with dictionary learning
The clustering of large numbers of heterogeneous features is a hot topic in multi-view
communities. Most existing multi-view clustering (MvC) methods employ matrix factorization …
communities. Most existing multi-view clustering (MvC) methods employ matrix factorization …