Deep graph clustering via dual correlation reduction
Deep graph clustering, which aims to reveal the underlying graph structure and divide the
nodes into different groups, has attracted intensive attention in recent years. However, we …
nodes into different groups, has attracted intensive attention in recent years. However, we …
Learnable graph convolutional network and feature fusion for multi-view learning
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 …
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …
Deep fusion clustering network
Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness
a strong tendency of combining autoencoder and graph neural networks to exploit structure …
a strong tendency of combining autoencoder and graph neural networks to exploit structure …
Partition level multiview subspace clustering
Multiview clustering has gained increasing attention recently due to its ability to deal with
multiple sources (views) data and explore complementary information between different …
multiple sources (views) data and explore complementary information between different …
Multiview subspace clustering via co-training robust data representation
Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …
[HTML][HTML] Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection
This paper introduces a new model to identify collective abnormal human behaviors from
large pedestrian data in smart cities. To accurately solve the problem, several algorithms …
large pedestrian data in smart cities. To accurately solve the problem, several algorithms …
Seeking commonness and inconsistencies: A jointly smoothed approach to multi-view subspace clustering
Multi-view subspace clustering aims to discover the hidden subspace structures from
multiple views for robust clustering, and has been attracting considerable attention in recent …
multiple views for robust clustering, and has been attracting considerable attention in recent …
Dealmvc: Dual contrastive calibration for multi-view clustering
Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
Late fusion multiple kernel clustering with proxy graph refinement
Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to
improve clustering performance. Among existing MKC algorithms, the recently proposed late …
improve clustering performance. Among existing MKC algorithms, the recently proposed late …
Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering
Multi-view non-negative matrix factorization (NMF) provides a reliable method to analyze
multiple views of data for low-dimensional representation. A variety of multi-view learning …
multiple views of data for low-dimensional representation. A variety of multi-view learning …