Breaking down multi-view clustering: a comprehensive review of multi-view approaches for complex data structures
Abstract Multi-View Clustering (MVC) is an emerging research area aiming to cluster
multiple views of the same data, which has recently drawn substantial attention. Various …
multiple views of the same data, which has recently drawn substantial attention. Various …
Incomplete multi-view clustering by simultaneously learning robust representations and optimal graph structures
Incomplete multi-view clustering aims to assign data samples into cohesive groups with
partially available information from multiple views. In this paper, we propose a novel …
partially available information from multiple views. In this paper, we propose a novel …
Multi-view clustering via pseudo-label guide learning and latent graph structure recovery
Multi-view clustering (MvC) accomplishes sample classification tasks by exploring
information from different views. Currently, researchers have paid greater attention to graph …
information from different views. Currently, researchers have paid greater attention to graph …
Consistent graph learning for multi-view spectral clustering
D Xie, Q Gao, Y Zhao, F Yang, W Song - Pattern Recognition, 2024 - Elsevier
Given the heterogeneous information of multiple views and the possible noise embedded in
multi-view data, it is difficult to directly learn a consistent representation from multiple graphs …
multi-view data, it is difficult to directly learn a consistent representation from multiple graphs …
Stratified multi-density spectral clustering using Gaussian mixture model
G Yue, A Deng, Y Qu, H Cui, X Wang - Information Sciences, 2023 - Elsevier
Spectral clustering aims to minimise inter-cluster similarity by constructing graph model,
which possesses a significant effect in data of arbitrary shape. Nonetheless, there are still …
which possesses a significant effect in data of arbitrary shape. Nonetheless, there are still …
A unified framework for fair spectral clustering with effective graph learning
X Zhang, Q Wang - arXiv preprint arXiv:2311.13766, 2023 - arxiv.org
We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …
samples from each sensitive group are approximately proportionally represented in each …
Elastic Deep Multi-view Autoencoder with Diversity Embedding
Current research on multi-view clustering (MVC) is pushing the boundaries of knowledge,
allowing the extraction of valuable insights from various points of view. Recently, many …
allowing the extraction of valuable insights from various points of view. Recently, many …
Similarity-Induced Weighted Consensus Laplacian Matrix Learning for Multiview Clustering
Multiview spectral clustering, which stands out with its remarkable clustering performance,
has drawn increasing research attention. Its core is properly weighing the contribution of …
has drawn increasing research attention. Its core is properly weighing the contribution of …
Efficient fuzzy-pruned high dimensional clustering with minimal distance measure
L Ghosh, D Konar - Expert Systems with Applications, 2024 - Elsevier
In this paper, we present a novel clustering approach that eliminates the need for predefined
cluster centres and cluster counts, addressing common limitations in traditional clustering …
cluster centres and cluster counts, addressing common limitations in traditional clustering …
Robust multilayer bootstrap networks in ensemble for unsupervised representation learning and clustering
XL Zhang, X Li - Pattern Recognition, 2024 - Elsevier
It is known that unsupervised nonlinear learning is sensitive to the selection of
hyperparameters, which hinders its practical use. How to determine the optimal …
hyperparameters, which hinders its practical use. How to determine the optimal …