Multi View Subspace Clustering

Multi-view subspace clustering aims to uncover underlying data structures and perform accurate clustering by integrating information from multiple data representations (views). Current research emphasizes developing scalable algorithms, often employing graph convolutional networks, kernel methods, or deep learning architectures, to efficiently handle large datasets and complex relationships between views. These advancements improve clustering accuracy and address challenges like high dimensionality and non-linear data structures, impacting fields where data is inherently multi-faceted, such as hyperspectral image analysis and network analysis. The focus is on creating robust and efficient methods that leverage both global and local data structures for improved clustering performance.