Multi-view subspace clustering via low-rank consensus projection matrix

Key Points

• Subspace clustering is enhanced using low-rank consensus projection matrix, improving the clustering process.
• Models using low-rank projections exhibit effective clustering across 5 different datasets, showcasing substantial advancements.
• Analysis with the proposed method demonstrates significant improvements over traditional clustering techniques.
• Potential applications include diverse fields like image processing and bioinformatics, indicating broader utility.