@inproceedings{e09cc284321f49b3967a4c77d2ec308c,
title = "Non-Negative Matrix Factorization of Clustered Data with Missing Values",
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.",
keywords = "imputation, missing values, non-negative matrix factorization, optimal recovery",
author = "Rebecca Chen and Varshney, {Lav R.}",
note = "Funding Information: This work was supported in part by Air Force STTR Grant FA8650-16-M-1819 and in part by grant number 2018-182794 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. Publisher Copyright: {\textcopyright} 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",
year = "2019",
month = jun,
doi = "10.1109/DSW.2019.8755555",
language = "English (US)",
series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "180--184",
booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",
address = "United States",
}