Abstract
We propose the approximation-theoretic technique of optimal recovery for imputing missing values in clustered data, specifically for non-negative matrix factorization (NMF), and develop an algorithm for implementation. Under certain geometric conditions, we prove tight upper bounds on NMF relative error, which is the first bound of this type for missing values. Experiments on image data and biological data show that this technique performs as well as or better than other imputation techniques that account for local structure.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 180-184 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728107080 |
| DOIs | |
| State | Published - Jun 2019 |
| Event | 2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: Jun 2 2019 → Jun 5 2019 |
Publication series
| Name | 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings |
|---|
Conference
| Conference | 2019 IEEE Data Science Workshop, DSW 2019 |
|---|---|
| Country | United States |
| City | Minneapolis |
| Period | 6/2/19 → 6/5/19 |
Keywords
- imputation
- missing values
- non-negative matrix factorization
- optimal recovery
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Artificial Intelligence
- Computer Networks and Communications
- Safety, Risk, Reliability and Quality
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Cite this
Chen, R., & Varshney, L. R. (2019). Non-Negative Matrix Factorization of Clustered Data with Missing Values. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 180-184). [8755555] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755555