# Binary Orthogonal Non-negative Matrix Factorization

arXiv: https://arxiv.org/abs/2210.10660 · HTML: https://mlsanity.com/paper/2210.10660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications](https://mlsanity.com/paper/1803.01257/) (2018)
- [k-Means Clustering Is Matrix Factorization](https://mlsanity.com/paper/1512.07548/) (2015)
- [Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization](https://mlsanity.com/paper/1407.7299/) (2014)
- [Reverse Annealing for Nonnegative/Binary Matrix Factorization](https://mlsanity.com/paper/2007.05565/) (2020)
- [Semi-supervised Nonnegative Matrix Factorization for Document Classification](https://mlsanity.com/paper/2203.03551/) (2022)

## Abstract (arXiv)

We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.