Learning Multi-layer Graphs and a Common Representation for Clustering

• URL: http://arxiv.org/abs/2010.12301v2
• Date: Wed, 3 Mar 2021 17:22:11 GMT
• Title: Learning Multi-layer Graphs and a Common Representation for Clustering
• Authors: Sravanthi Gurugubelli and Sundeep Prabhakar Chepuri
• Abstract summary: We focus on graph learning from multi-view data of shared entities for spectral clustering. We propose an efficient solver based on alternating minimization to solve the problem. Numerical experiments on synthetic and real datasets demonstrate that the proposed algorithm outperforms state-of-the-art multi-view clustering techniques.
• Score: 13.90938823562779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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