Multi View Subspace

Multi-view subspace clustering aims to uncover underlying data structures from multiple, potentially heterogeneous, data sources by identifying low-dimensional subspaces where data points cluster. Current research emphasizes developing efficient and robust algorithms, often employing deep learning architectures like autoencoders and attention mechanisms, or leveraging kernel methods and graph-based approaches to handle large datasets and non-linear relationships. These advancements improve clustering accuracy and scalability, impacting applications such as image analysis, network analysis, and hyperspectral image processing where multi-view data is prevalent. The field is actively exploring ways to better integrate complementary and inconsistent information across views for improved robustness and performance.