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 …
Generalized nonconvex low-rank tensor approximation for multi-view subspace clustering
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 …
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks
High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
Tensorial multi-view clustering via low-rank constrained high-order graph learning
Multi-view clustering aims to partition multi-view data into different categories by optimally
exploring the consistency and complementary information from multiple sources. However …
exploring the consistency and complementary information from multiple sources. However …
Comprehensive multi-view representation learning
Abstract Recently, Multi-view Representation Learning (MRL) has drawn immense
attentions in the analysis of multi-source data and ubiquitously employed across different …
attentions in the analysis of multi-source data and ubiquitously employed across different …
Cross-view graph matching guided anchor alignment for incomplete multi-view clustering
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 …
establish a connection with original samples to generate the bipartite graphs for clustering …
Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning
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 …
subspace clustering tensor learning method based on Markov chain is a crucial branch of …
Tensorized incomplete multi-view clustering with intrinsic graph completion
Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a
consensus representation from different views but ignore the important information hidden in …
consensus representation from different views but ignore the important information hidden in …
Adaptive transition probability matrix learning for multiview spectral clustering
Multiview clustering as an important unsupervised method has been gathering a great deal
of attention. However, most multiview clustering methods exploit the self-representation …
of attention. However, most multiview clustering methods exploit the self-representation …
Efficient and robust multiview clustering with anchor graph regularization
Multi-view clustering has received widespread attention owing to its effectiveness by
integrating multi-view data appropriately, but traditional algorithms have limited applicability …
integrating multi-view data appropriately, but traditional algorithms have limited applicability …