Feature extraction via multi-view non-negative matrix factorization with local graph regularization
Feature extraction is a crucial and difficult issue in pattern recognition tasks with the high-dimensional and multiple features. To extract the latent structure of multiple features without label information, multi-view learning algorithms have been developed. In this paper, motivated by manifold learning and multi-view Non-negative Matrix Factorization (NM-F), we introduce a novel feature extraction method via multi-view NMF with local graph regularization, where the inner-view relatedness between data is taken into consideration. We propose the matrix factorization objective function by constructing a nearest neighbor graph to integrate local geometrical information of each view and apply two iterative updating rules to effectively solve the optimization problem. In the experiment, we use the extracted feature to cluster several realistic datasets. The experimental results demonstrate the effectiveness of our proposed feature extraction approach.