Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning

TL;DR

This paper introduces a symmetric nonnegative matrix factorization algorithm enhanced with self-paced learning, which improves clustering accuracy by effectively distinguishing normal and abnormal samples across diverse datasets.

Contribution

It presents a novel self-paced learning-based approach with weight constraints for symmetric nonnegative matrix factorization, enhancing clustering performance and robustness.

Findings

Effective in distinguishing normal and abnormal samples

Improves clustering accuracy on image and text datasets

Demonstrates robustness through experimental validation

Abstract

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.