通过自步学习提升非负矩阵分解的聚类效果，更好区分正常与异常样本。
Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning
- 引入难度权重变量，结合硬软约束优化模型训练顺序。
- 在图像与文本数据集上验证，显著提升异常样本识别能力。
- 适合需要精准区分正常/异常数据的场景，如工业质检、故障检测。
提出一种基于自步学习的对称非负矩阵分解算法，旨在提升模型的聚类性能，使其能以误差驱动的方式更有效地区分正常样本与异常样本。该方法为所有样本分配一个衡量其难度的权重变量，并通过硬权重与软权重双重策略对其进行约束，确保模型合理性。在多个图像与文本数据集上进行了聚类分析，实验结果验证了所提算法的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。