基于自适应图调节和低秩矩阵分解的鲁棒聚类

聚类是数据挖掘和机器学习领域的重要研究内容,一般会先基于数据样本构建相似图,再基于相似图将样本划分到相应的类中。但是真实的数据经常被损坏,导致学习的相似图不准确,从而直接影响聚类结果。为解决这些问题,提出一种面向鲁棒聚类的自适应图调节和低秩矩阵分解的方法,该方法的核心思想是:将原始数据X分解为纯净数据D和噪声数据S,再基于纯净数据构造拉普拉斯矩阵并进行自适应图调节。随后,给出一个联合学习框架,将数据分离、自适应图正则、噪声消除和低秩矩阵分解集成到一个目标函数中。利用增广拉格朗日乘子法分别更新变量。最后,在理论上证明算法的收敛性并进行实验。实验结果表明所提出的方法与现有一些方法相比有一定优越性。

Robust clustering based on adaptive graph regularization and low-rank matrix decomposition

Clustering is an important research content in the field of data mining and machine learning. Generally, similarity graphs are constructed based on data samples, and then the samples are divided into corresponding classes based on the similarity graphs. However, the real data is often damaged, resulting in inaccurate similarity graphs, which directly affects the clustering results. In order to solve these problems, a robust clustering-oriented adaptive graph adjustment and low-rank matrix decomposition method is proposed. The core idea of the method is to decompose the original data X into pure data D and noisy data S, then construct a Laplacian matrix based on pure data and perform adaptive graph adjustment. Subsequently, a joint learning framework is given, which integrates data separation, adaptive graph regularization, noise removal and low-rank matrix decomposition into an objective function. Use augmented Lagrangian multiplier method to update variables separately. Finally, the paper theoretically proves the convergence of the algorithm and conduct experiments. The experimental results show that the proposed method has certain advantages compared with some existing methods.