EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization
This work addresses data integration challenges in clustering applications, particularly for multiview imaging, but appears incremental as it builds on existing NMF frameworks with added regularization.
The authors tackled the problem of multiview data integration for clustering by developing EquiNMF, a graph-regularized multiview nonnegative matrix factorization method, and showed it consistently outperforms other NMF methods on multiview imaging data.
Nonnegative matrix factorization (NMF) methods have proved to be powerful across a wide range of real-world clustering applications. Integrating multiple types of measurements for the same objects/subjects allows us to gain a deeper understanding of the data and refine the clustering. We have developed a novel Graph-reguarized multiview NMF-based method for data integration called EquiNMF. The parameters for our method are set in a completely automated data-specific unsupervised fashion, a highly desirable property in real-world applications. We performed extensive and comprehensive experiments on multiview imaging data. We show that EquiNMF consistently outperforms other single-view NMF methods used on concatenated data and multi-view NMF methods with different types of regularizations.