  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. Moreover, a
series of large-scale algorithms that bear one or more hyperparameters are
impractical in real-world applications. To address the two issues, we propose
an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC
first learns coefficient matrices from corresponding base matrices of different
dimensions, then fuses them to obtain an optimal consensus matrix. By mapping
original features into distinctive low-dimensional spaces, we can attain more
comprehensive knowledge, thus obtaining better clustering results. Moreover, we
design a six-step alternative optimization algorithm proven to be convergent
theoretically. Also, AWMVC shows excellent performance on various benchmark
datasets compared with existing ones. The code of AWMVC is publicly available
at https://github.com/wanxinhang/AAAI-2023-AWMVC.
