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

05/08/2022
by   Zongmo Huang, et al.
0

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.

READ FULL TEXT
research
07/26/2020

Deep Embedded Multi-view Clustering with Collaborative Training

Multi-view clustering has attracted increasing attentions recently by ut...
research
09/30/2022

Double Graphs Regularized Multi-view Subspace Clustering

Recent years have witnessed a growing academic interest in multi-view su...
research
05/01/2021

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

Multi-view clustering is an important yet challenging task in machine le...
research
12/09/2022

Multi-view Graph Convolutional Networks with Differentiable Node Selection

Multi-view data containing complementary and consensus information can f...
research
12/29/2021

Deep Graph Clustering via Dual Correlation Reduction

Deep graph clustering, which aims to reveal the underlying graph structu...
research
02/25/2022

Improved Dual Correlation Reduction Network

Deep graph clustering, which aims to reveal the underlying graph structu...
research
05/31/2023

Learning Representations without Compositional Assumptions

This paper addresses unsupervised representation learning on tabular dat...

Please sign up or login with your details

Forgot password? Click here to reset