Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency.