Auto-Weighted Multi-View Clustering for Large-Scale Data

Abstract

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.

The methods for improving large-scale multi-view clustering efficiency: a survey

“…The original multi-view data is factorized to learn a unified discrete indicator matrix containing the clustering results. Wan et al (2023b) and Wan et al (2023a) considered that the dimension of different view data may be inconsistent, and the fixed-dimension consensus matrix learned based on matrix factorization will destroy the exploration of complementary information. They proposed to first learn multiple dimension embedding matrices for each view based on matrix factorization.…”

“…They proposed to first learn multiple dimension embedding matrices for each view based on matrix factorization. Then Wan et al (2023b) obtained the final consensus matrix through projection matrix and the final result is obtained by using k-means on the consensus matrix. However, Wan et al (2023a) unified the new k-means to the optimization objective to obtain clustering results directly (Pei et al 2022).…”

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

“…By treating samples as nodes and relationships between samples as edges, GNNs can easily capture the underlying relationships and rules between samples through message propagation mechanisms, which are suitable to various types of graphs [9,26,38,41,43,44]. GNNs have gained significant popularity and are widely employed in various real-world applications, including recommendation [81], community discovery [25,50], fake news detection [29,85], multi-view clustering [24,74,78,92], bioinformatics [22], hyper-graph analysis [82], image processing [27,30], etc, because they can find the relationship between samples in changing and multivariate data [28,75,88].…”