@inproceedings{c181ca9f334547f4bc8b7fd451ea1948,
title = "Nonnegative matrix factorization using ADMM: Algorithm and convergence analysis",
abstract = "The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a popular algorithm based on the alternating direction method of multipliers proposed in Boyd et al 2011. We show that the algorithm converges globally to the set of KKT solutions whenever certain penalty parameter ρ satisfies ρ > 1. We further extend the algorithm and its analysis to the problem where the observation matrix contains missing values. Numerical experiments on real and synthetic data sets demonstrate the effectiveness of the algorithms under investigation.",
keywords = "ADMM, Convergence Analysis, Nonconvex Optimization, Nonnegative Matrix Factorization",
author = "Davood Hajinezhad and Chang, \{Tsung Hui\} and Xiangfeng Wang and Qingjiang Shi and Mingyi Hong",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",
year = "2016",
month = may,
day = "18",
doi = "10.1109/ICASSP.2016.7472577",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4742--4746",
booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings",
address = "美国",
}