Learning Multi-layer Graphs and a Common Representation for Clustering
This addresses multi-view clustering for data analysis, but it is incremental as it builds on existing graph learning and spectral clustering methods.
The paper tackles the problem of learning multi-layer graphs from multi-view data for spectral clustering, proposing a method that jointly estimates graph Laplacians and a common low-dimensional embedding, and it outperforms state-of-the-art techniques in experiments on synthetic and real datasets.
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.