Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs

TL;DR

This paper introduces DMVCJ, a deep multi-view clustering method that jointly learns latent representations and graphs, enhancing clustering accuracy and robustness by leveraging graph convolution networks and a sample-weighting strategy.

Contribution

The paper proposes a novel joint learning framework for latent representations and graphs in multi-view clustering, integrating GCNs and a noise-robust sample-weighting mechanism.

Findings

Significant improvement in clustering performance on real-world datasets.

Effective noise reduction through the sample-weighting strategy.

Demonstrated robustness and adaptability across multiple multi-view datasets.

Abstract

With the representation learning capability of the deep learning models, deep embedded multi-view clustering (MVC) achieves impressive performance in many scenarios and has become increasingly popular in recent years. Although great progress has been made in this field, most existing methods merely focus on learning the latent representations and ignore that learning the latent graph of nodes also provides available information for the clustering task. To address this issue, in this paper we propose Deep Embedded Multi-view Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ), which utilizes the latent graphs to promote the performance of deep embedded MVC models from two aspects. Firstly, by learning the latent graphs and feature representations jointly, the graph convolution network (GCN) technique becomes available for our model. With the capability of GCN in exploiting the information from both graphs and features, the clustering performance of our model is significantly promoted. Secondly, based on the adjacency relations of nodes shown in the latent graphs, we design a sample-weighting strategy to alleviate the noisy issue, and further improve the effectiveness and robustness of the model. Experimental results on different types of real-world multi-view datasets demonstrate the effectiveness of DMVCJ.