Nonlinear multi-view clustering for non-negative matrix factorization

Abstract

Deep multi-view clustering methods have made significant progress in recent years, benefiting from their wide parameter space and the ability to consider more details in the learning procedure. However, the existing methods suffer from the following problems: (1) Deep models based on data-driven learning often involve opaque update processes and struggle to maintain stability across varying data scales. (2) Non-negative Matrix Factorization (NMF)-based clustering methods generally exhibit limited robustness due to their restricted nonlinear fitting capabilities and narrow parameter spaces. To address these issues, we propose a nonlinear non-negative matrix factorization multi-view clustering framework. Our framework integrates traditional NMF optimization principles into a deep model to enhance interpretability and stability. In addition, it employs partially parameterized NMF iterations to improve nonlinear fitting ability, thereby expanding the parameter space and enhancing model robustness. We also introduce a cross-view contrastive loss to guide the model in learning inter-view diversity and cluster-friendly structural features. Experiments on multiple datasets show that our method outperforms state-of-the-art clustering methods.