论文标题
学习多层图和聚类的共同表示形式
Learning Multi-layer Graphs and a Common Representation for Clustering
论文作者
论文摘要
在本文中，我们关注从共享实体的多视图数据进行频谱聚类的图形学习。我们可以使用多层图形图与代表共享实体的多层图进行多层数据中的实体之间的交互。不同层的边缘捕获了实体的关系。假设一个平滑度数据模型，我们共同估计单个图层的图形拉普拉斯矩阵和公共顶点集的低维嵌入。我们限制图形拉普拉斯矩阵的等级，以获得用于聚类的多组分图层。所有观点共有的低维节点嵌入，吸收了视图中存在的互补信息。我们提出了一个基于交替最小化的有效求解器，以解决所提出的多层多组分图学习问题。关于合成和真实数据集的数值实验表明，所提出的算法优于最先进的多视图聚类技术。
In this paper, we focus on graph learning from multi-view data of shared entities for spectral clustering. We can explain interactions between the entities in multi-view data using a multi-layer graph with a common vertex set, which represents the shared entities. The edges of different layers capture the relationships of the entities. Assuming a smoothness data model, we jointly estimate the graph Laplacian matrices of the individual graph layers and low-dimensional embedding of the common vertex set. We constrain the rank of the graph Laplacian matrices to obtain multi-component graph layers for clustering. The low-dimensional node embeddings, common to all the views, assimilate the complementary information present in the views. We propose an efficient solver based on alternating minimization to solve the proposed multi-layer multi-component graph learning problem. Numerical experiments on synthetic and real datasets demonstrate that the proposed algorithm outperforms state-of-the-art multi-view clustering techniques.
