Non-Negative Matrix Factorization of Clustered Data with Missing Values

Rebecca Chen; Lav R Varshney

doi:10.1109/DSW.2019.8755555

Non-Negative Matrix Factorization of Clustered Data with Missing Values

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/DSW.2019.8755555
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

Fingerprint

Keywords

imputation
missing values
non-negative matrix factorization
optimal recovery

ASJC Scopus subject areas

Computer Networks and Communications
Safety, Risk, Reliability and Quality
Computational Theory and Mathematics
Artificial Intelligence

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

Non-Negative Matrix Factorization of Clustered Data with Missing Values. / Chen, Rebecca; Varshney, Lav R.

2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 180-184 8755555 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, R & Varshney, LR 2019, Non-Negative Matrix Factorization of Clustered Data with Missing Values. in 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings., 8755555, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 180-184, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 6/2/19. https://doi.org/10.1109/DSW.2019.8755555

@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}",

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",

note = "2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",

}

TY - GEN

T1 - Non-Negative Matrix Factorization of Clustered Data with Missing Values

AU - Chen, Rebecca

AU - Varshney, Lav R

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - imputation

KW - missing values

KW - non-negative matrix factorization

KW - optimal recovery

UR - http://www.scopus.com/inward/record.url?scp=85069536911&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069536911&partnerID=8YFLogxK

U2 - 10.1109/DSW.2019.8755555

DO - 10.1109/DSW.2019.8755555

M3 - Conference contribution

AN - SCOPUS:85069536911

T3 - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

SP - 180

EP - 184

BT - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Data Science Workshop, DSW 2019

Y2 - 2 June 2019 through 5 June 2019

ER -

Access to Document

10.1109/DSW.2019.8755555