Re-weighted multi-view clustering via triplex regularized non-negative matrix factorization

Multi-view clustering, which aims at dividing data with similar structures into their respective groups, is a popular research subject in computer vision and machine learning. In recent years, Non-negative matrix factorization (NMF) has received constant concern in multi-view clustering due to its ability to deal with high-dimensional data. However, most existing NMF methods may fail to integrate valuable information from multi-view data adequately, and the local geometry structure in data is also not fully considered. Thus, it's still a crucial but challenging problem, which effectively extracts multi-view information while maintaining the low-dimensional geometry structure. In this paper, we propose an innovative multi-view clustering method, referred to as re-weighted multi-view clustering via triplex regularized non-negative matrix factorization (SMCTN), which is a unified framework and provides the following contributions: 1) pairwise regularization can extract complementary information between views and is suitable for both homogeneous and heterogeneous perspectives; 2) consensus regularization can process the consistent information between views; 3) graph regularization can preserve the geometric structure of data. Specifically, SMCTN applies a re-weighted strategy to assign suitable weights for multiple views according to their contributions. Besides, an effective iterative updating algorithm is developed to solve the non convex optimization problem in SMCTN. Extensive experimental results on textual and image datasets indicate that the superior performance of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.