  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.
