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

• URL: http://arxiv.org/abs/2205.03803v1
• Date: Sun, 8 May 2022 07:40:21 GMT
• Title: Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs
• Authors: Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He
• Abstract summary: We propose Deep Embedded Multi-view Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ) By learning the latent graphs and feature representations jointly, the graph convolution network (GCN) technique becomes available for our model. Based on the adjacency relations of nodes shown in the latent graphs, we design a sample-weighting strategy to alleviate the noisy issue.
• Score: 13.052394521739192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Language Models are Graph Learners [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• Disentangled Generative Graph Representation Learning [51.59824683232925]
  This paper introduces DiGGR (Disentangled Generative Graph Representation Learning), a self-supervised learning framework. It aims to learn latent disentangled factors and utilize them to guide graph mask modeling. Experiments on 11 public datasets for two different graph learning tasks demonstrate that DiGGR consistently outperforms many previous self-supervised methods.
  arXiv  Detail & Related papers  (2024-08-24T05:13:02Z)
• SLRL: Structured Latent Representation Learning for Multi-view Clustering [24.333292079699554]
  Multi-View Clustering (MVC) aims to exploit the inherent consistency and complementarity among different views to improve clustering outcomes. Despite extensive research in MVC, most existing methods focus predominantly on harnessing complementary information across views to enhance clustering effectiveness. We introduce a novel framework, termed Structured Latent Representation Learning based Multi-View Clustering method.
  arXiv  Detail & Related papers  (2024-07-11T09:43:57Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• GraphLearner: Graph Node Clustering with Fully Learnable Augmentation [76.63963385662426]
  Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters. We propose a Graph Node Clustering with Fully Learnable Augmentation, termed GraphLearner. It introduces learnable augmentors to generate high-quality and task-specific augmented samples for CDGC.
  arXiv  Detail & Related papers  (2022-12-07T10:19:39Z)
• Dual Information Enhanced Multi-view Attributed Graph Clustering [11.624319530337038]
  A novel Dual Information enhanced multi-view Attributed Graph Clustering (DIAGC) method is proposed in this paper. The proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views. The Mutual Information Maximization (MIM) module maximizes the agreement between the latent high-level representation and low-level ones, and enables the high-level representation to satisfy the desired clustering structure.
  arXiv  Detail & Related papers  (2022-11-28T01:18:04Z)
• Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data [52.491074276133325]
  Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. We propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
  arXiv  Detail & Related papers  (2022-11-08T09:19:32Z)
• Deep Contrastive Learning for Multi-View Network Embedding [20.035449838566503]
  Multi-view network embedding aims at projecting nodes in the network to low-dimensional vectors. Most contrastive learning-based methods mostly rely on high-quality graph embedding. We design a novel node-to-node Contrastive learning framework for Multi-view network Embedding (CREME)
  arXiv  Detail & Related papers  (2021-08-16T06:29:18Z)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning [48.09362183184101]
  We propose a novel self-supervised approach to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. Our method achieves new state-of-the-art results and surpasses some semi-supervised counterparts by large margins.
  arXiv  Detail & Related papers  (2021-05-12T14:20:13Z)
• Consistent Multiple Graph Embedding for Multi-View Clustering [41.17336912278538]
  We propose a novel Consistent Multiple Graph Embedding Clustering framework(CMGEC) Specifically, a multiple graph auto-encoder is designed to flexibly encode the complementary information of multi-view data. To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module(MMIM) is introduced.
  arXiv  Detail & Related papers  (2021-05-11T09:08:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.