Name
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. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Data integration,Graph,Mathematical optimization,Pattern recognition,Concatenation,Non-negative matrix factorization,Artificial intelligence,Cluster analysis,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1409.4018 | 5 |
PageRank | References | Authors |
0.47 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Hidru | 1 | 7 | 0.83 |
| Anna Goldenberg | 2 | 276 | 26.12 |