Auto-weighted Multi-view Clustering for Large-scale Data

Understand

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.

• Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data.
• Matrix factorization-based models are a representative of solving this problem.
• However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability.