EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization

TL;DR

EquiNMF is a novel graph-regularized multiview NMF method that automatically adapts parameters for data integration, improving clustering performance across multiview datasets.

Contribution

We introduce EquiNMF, an unsupervised, automated parameter setting multiview NMF approach with graph regularization for enhanced data integration.

Findings

EquiNMF outperforms single-view NMF on concatenated data.

EquiNMF surpasses other multi-view NMF methods with different regularizations.

The method is effective on multiview imaging datasets.

Abstract

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.