通过自表示动态学习图结构，提升概念分解的聚类性能
Concept Factorization via Self-Representation and Adaptive Graph Structure Learning
- 用自表示方法学习数据间亲和关系，构建动态图结构
- 在四个真实数据集上优于现有最优模型，聚类效果更优
- 适合需要自适应图结构的高维数据聚类任务
概念分解（CF）模型因其在数据聚类中的优异表现受到广泛关注。近年来，基于CF的多种变体模型通过考虑数据集的内在几何流形结构并引入图正则化技术，在聚类任务中取得显著进展。然而，其聚类性能高度依赖初始图结构的构建。为实现数据图结构的自适应学习，本文提出基于自表示与自适应图结构学习的概念分解模型（CFSRAG）。该模型通过自表示方法学习数据间的亲和关系，并利用所得亲和矩阵实现动态图正则化约束，从而保证对数据内部几何结构的动态建模。本文给出了CFSRAG的更新规则与收敛性分析，并在四个真实数据集上进行了对比实验。结果表明，所提模型在聚类性能上优于当前主流先进模型。
原文摘要 · Abstract (English)
Concept Factorization (CF) models have attracted widespread attention due to their excellent performance in data clustering. In recent years, many variant models based on CF have achieved great success in clustering by taking into account the internal geometric manifold structure of the dataset and using graph regularization techniques. However, their clustering performance depends greatly on the construction of the initial graph structure. In order to enable adaptive learning of the graph structure of the data, we propose a Concept Factorization Based on Self-Representation and Adaptive Graph Structure Learning (CFSRAG) Model. CFSRAG learns the affinity relationship between data through a self-representation method, and uses the learned affinity matrix to implement dynamic graph regularization constraints, thereby ensuring dynamic learning of the internal geometric structure of the data. Finally, we give the CFSRAG update rule and convergence analysis, and conduct comparative experiments on four real datasets. The results show that our model outperforms other state-of-the-art models.
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