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arXiv · 1409.4018

EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization

Daniel Hidru·Anna Goldenberg
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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.

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Daniel Hidru, Anna Goldenberg. 2014-09-14. EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization. https://arxiv.org/abs/1409.4018

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