Balanced Multi-View Clustering

Multi-view clustering (MvC, a technique that combines information from multiple data sources to improve clustering) often fails to use all available information effectively because some data views dominate the learning process while others are neglected. This paper proposes Balanced Multi-View Clustering (BMvC), which uses view-specific contrastive regularization (VCR, a technique that adjusts how each data source is learned to maintain balanced importance) to ensure all views contribute fairly to the final clustering result.