DGNMF: Dynamic Diffusion Graph Nonnegative Matrix Factorization.

Abstract

In feature learning (FL), structural information shows advantages in retaining information and maintaining stability. Graph diffusion, a graph learning method that can focus on neighborhood structure and transmit information, has great research potential. In this study, a novel dynamic diffusion graph nonnegative matrix factorization (DGNMF) method is proposed, which uses a diffusion graph to improve the performance of FL and further enhances the effectiveness and stability of downstream classification tasks. DGNMF aims to mine and retain structural information more deeply in FL to build a more powerful and stable FL method. First, the model embeds graph learning into FL to obtain features containing structural information. Second, dynamic diffusion graph learning is used to mine deeper and more global structural information. Finally, we construct an updateable indicator matrix to enhance the discriminability of features. The classification experimental results of DGNMF on six databases demonstrate its advantages, verify its effectiveness and stability, and prove the importance of diffusion graph in improving FL.