Multi-view and Multi-order Graph Clustering via Constrained l1, 2-norm
The graph-based multi-view clustering algorithms achieve decent clustering performance by
consensus graph learning of the first-order graphs from different views. However, the first …
consensus graph learning of the first-order graphs from different views. However, the first …
New approach for learning structured graph with Laplacian rank constraint
Many existing graph-clustering methods get results in a two-stage manner, including
constructing a graph from data and partitioning it. It always leads to sub-optimal …
constructing a graph from data and partitioning it. It always leads to sub-optimal …
Dual auto-weighted multi-view clustering via autoencoder-like nonnegative matrix factorization
SJ Xiang, HC Li, JH Yang, XR Feng - Information Sciences, 2024 - Elsevier
Multi-view clustering (MVC) can exploit the complementary information among multi-view
data to achieve the satisfactory performance, thus having extensive potentials for practical …
data to achieve the satisfactory performance, thus having extensive potentials for practical …
[HTML][HTML] Hierarchical Prototype-Aligned Graph Neural Network for Cross-Scene Hyperspectral Image Classification
D Shen, H Hu, F He, F Zhang, J Zhao, X Shen - Remote Sensing, 2024 - mdpi.com
The objective of cross-scene hyperspectral image (HSI) classification is to develop models
capable of adapting to the “domain gap” that exists between different scenes, enabling …
capable of adapting to the “domain gap” that exists between different scenes, enabling …
Robust auto-weighted and dual-structural representation learning for image clustering
K Jiang, Z Liu, Q Sun - Journal of Electronic Imaging, 2024 - spiedigitallibrary.org
High-dimensional data samples tend to contain highly correlated features and are quite
fragile to various noises and outliers in practical applications. For subspace clustering …
fragile to various noises and outliers in practical applications. For subspace clustering …