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With the progress of science and technology, multi-view clustering exploring heterogeneous information among diverse views has been widely employed in real-world applications. Nonnegative Matrix Factorization (NMF) has received wide attention because of its interpretability. However, existing multi-view clustering methods based on NMF are vulnerable to outliers and noise. To alleviate these problems, we propose a novel model, named graph regularized multiple nonnegative matrix factorization with L2,1 norm (GRMNMF) method, for exploring multi-view data. GRMNMF utilizes L2,1 norm to calculate the error between the original data and the reconstructed data and simultaneously utilizes the geometrical structure of data space. A series of experiments were conducted in seven real data sets. The experimental results manifest the superiority of GRMNMF.
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