Scalable Multi-view Clustering with Graph Filtering

• URL: http://arxiv.org/abs/2205.09228v1
• Date: Wed, 18 May 2022 22:04:23 GMT
• Title: Scalable Multi-view Clustering with Graph Filtering
• Authors: Liang Liu and Peng Chen and Guangchun Luo and Zhao Kang and Yonggang Luo and Sanchu Han
• Abstract summary: We propose a generic framework to cluster both attribute and graph data with heterogeneous features. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors.
• Score: 18.83570662893356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordingly, in this paper, we propose a generic framework to cluster both attribute and graph data with heterogeneous features. It is capable of exploring the interplay between feature and structure. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise to achieve a clustering-friendly smooth representation. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors. Extensive experiments on attribute and graph benchmarks demonstrate the superiority of our approach with respect to state-of-the-art approaches.

Related papers

• Discriminative Anchor Learning for Efficient Multi-view Clustering [59.11406089896875]
  We propose discriminative anchor learning for multi-view clustering (DALMC) We learn discriminative view-specific feature representations according to the original dataset. We build anchors from different views based on these representations, which increase the quality of the shared anchor graph.
  arXiv  Detail & Related papers  (2024-09-25T13:11:17Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Efficient Multi-View Graph Clustering with Local and Global Structure Preservation [59.49018175496533]
  We propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG) Specifically, EMVGC-LG jointly optimize anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number.
  arXiv  Detail & Related papers  (2023-08-31T12:12:30Z)
• Deep Multi-View Subspace Clustering with Anchor Graph [11.291831842959926]
  We propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG) DMCAG learns the embedded features for each view independently, which are used to obtain the subspace representations. Our method achieves superior clustering performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-11T16:17:43Z)
• 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)
• High-order Multi-view Clustering for Generic Data [15.764819403555512]
  Graph-based multi-view clustering has achieved better performance than most non-graph approaches. We introduce an approach called high-order multi-view clustering (HMvC) to explore the topology structure information of generic data.
  arXiv  Detail & Related papers  (2022-09-22T07:49:38Z)
• Multi-view Contrastive Graph Clustering [12.463334005083379]
  We propose a generic framework to cluster multi-view attributed graph data. Inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method. Our simple approach outperforms existing deep learning-based methods.
  arXiv  Detail & Related papers  (2021-10-22T15:22:42Z)
• 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)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Smoothed Multi-View Subspace Clustering [14.77544837600836]
  We propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) It employs a novel technique, i.e., graph filtering, to obtain a smooth representation for each view. Experiments on benchmark datasets validate the superiority of our approach.
  arXiv  Detail & Related papers  (2021-06-18T02:24:19Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

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.