# Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning

arXiv: https://arxiv.org/abs/2410.15306 · HTML: https://mlsanity.com/paper/2410.15306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/1811.05642/) (2018)
- [Truncated Cauchy Non-negative Matrix Factorization](https://mlsanity.com/paper/1906.00495/) (2019)
- [Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/1509.01404/) (2015)

## Abstract (arXiv)

A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model. It could make the model better distinguish normal samples from abnormal samples in an error-driven way. A weight variable that could measure the degree of difficulty to all samples was assigned in this method, and the variable was constrained by adopting both hard-weighting and soft-weighting strategies to ensure the rationality of the model. Cluster analysis was carried out on multiple data sets such as images and texts, and the experimental results showed the effectiveness of the proposed algorithm.