Abstract
Rank selection, i.e. the choice of factorization rank, is the first step in constructing Nonnegative Matrix Factorization (NMF) models. It is a long-standing problem which is not unique to NMF, but arises in most models which attempt to decompose data into its underlying components. Since these models are often used in the unsupervised setting, the rank selection problem is further complicated by the lack of ground truth labels. In this paper, we review and empirically evaluate the most commonly used schemes for NMF rank selection.
| Original language | English |
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| Title of host publication | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
| Editors | Wei Ding, Chang-Tien Lu, Fusheng Wang, Liping Di, Kesheng Wu, Jun Huan, Raghu Nambiar, Jundong Li, Filip Ilievski, Ricardo Baeza-Yates, Xiaohua Hu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1294-1301 |
| Number of pages | 8 |
| ISBN (Electronic) | 9798350362480 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 2024 IEEE International Conference on Big Data, BigData 2024 - Washington, United States Duration: Dec 15 2024 → Dec 18 2024 |
Publication series
| Name | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
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Conference
| Conference | 2024 IEEE International Conference on Big Data, BigData 2024 |
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| Country/Territory | United States |
| City | Washington |
| Period | 12/15/24 → 12/18/24 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DEAC05-00OR22725 with the U.S. Department of Energy. This material was based upon work supported by the U.S. Department of Energy, Office of Science under Contract DE-AC02-06CH11357 at Argonne National Laboratory. This work is supported by the National Science Foundation under Grant Nos. OAC-2106920 and OAC-2106738. The publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Nonnegative Matrix Factorization
- Rank selection