Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications

2018/03/31 by Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos +1 · 232 citations
Computer Science · Engineering · Mathematics · #Face and Expression Recognition #Factor (programming language) #Factorization #Matrix (chemical analysis) #Matrix algebra #Matrix decomposition #Non-negative matrix factorization #Nonnegative matrix #Remote-Sensing Image Classification #Signal processing #Tensor decomposition and applications #cs.LG #eess.SP #stat.ML

paper · pdf · doi:10.1109/msp.2018.2877582

published in IEEE Signal Processing Magazine 36(2), 59-80 (Institute of Electrical and Electronics Engineers) · accepted version, IEEE Signal Processing Magazine; supplementary materials added. Some minor revisions implemented

openalex created_date 2018/03/29 · arxiv created 2018/11/16 · openalex publication_date 2019/02/26 · arxiv updated 2019/03/27 · openalex updated_date 2026/08/06

Abstract

Nonnegative matrix factorization (NMF) aims to factor a data matrix into low-rank latent factor matrices with nonnegativity constraints.

Citations

• Nonnegative Matrix Factorization: A Comprehensive Review
• Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering