A Non-Negative Matrix Factorization Game
Abstract
We present a novel game-theoretic formulation of Non-Negative Matrix Factorization (NNMF), a popular data-analysis method with many scientific and engineering applications. The game-theoretic formulation is shown to have favorable scaling and parallelization properties, while retaining reconstruction and convergence performance comparable to the traditional Multiplicative Updates (Lee & Seung, 1999) algorithm.
Cite
Text
Singh. "A Non-Negative Matrix Factorization Game." ICLR 2022 Workshops: GMS, 2022.Markdown
[Singh. "A Non-Negative Matrix Factorization Game." ICLR 2022 Workshops: GMS, 2022.](https://mlanthology.org/iclrw/2022/singh2022iclrw-nonnegative/)BibTeX
@inproceedings{singh2022iclrw-nonnegative,
title = {{A Non-Negative Matrix Factorization Game}},
author = {Singh, Satpreet Harcharan},
booktitle = {ICLR 2022 Workshops: GMS},
year = {2022},
url = {https://mlanthology.org/iclrw/2022/singh2022iclrw-nonnegative/}
}