Structure-guided Deep Multi-View Clustering

• URL: http://arxiv.org/abs/2501.10157v1
• Date: Fri, 17 Jan 2025 12:42:30 GMT
• Title: Structure-guided Deep Multi-View Clustering
• Authors: Jinrong Cui, Xiaohuang Wu, Haitao Zhang, Chongjie Dong, Jie Wen,
• Abstract summary: Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. Most of the existing clustering methods often neglect to fully mine multi-view structural information. We propose a structure-guided deep multi-view clustering model to explore the distribution of multi-view data.
• Score: 13.593229506936682
• License:
• Abstract: Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performance. To address these limitations, we propose a structure-guided deep multi-view clustering model. Specifically, we introduce a positive sample selection strategy based on neighborhood relationships, coupled with a corresponding loss function. This strategy constructs multi-view nearest neighbor graphs to dynamically redefine positive sample pairs, enabling the mining of local structural information within multi-view data and enhancing the reliability of positive sample selection. Additionally, we introduce a Gaussian distribution model to uncover latent structural information and introduce a loss function to reduce discrepancies between view embeddings. These two strategies explore multi-view structural information and data distribution from different perspectives, enhancing consistency across views and increasing intra-cluster compactness. Experimental evaluations demonstrate the efficacy of our method, showing significant improvements in clustering performance on multiple benchmark datasets compared to state-of-the-art multi-view clustering approaches.

Related papers

• Balanced Multi-view Clustering [56.17836963920012]
  Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information. We propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view.
  arXiv  Detail & Related papers  (2025-01-05T14:42:47Z)
• 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)
• One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering [42.92751228313385]
  We propose a novel multi-view clustering model, named Dual-space Co-training Large-scale Multi-view Clustering (DSCMC) The main objective of our approach is to enhance the clustering performance by leveraging co-training in two distinct spaces. Our algorithm has an approximate linear computational complexity, which guarantees its successful application on large-scale datasets.
  arXiv  Detail & Related papers  (2024-01-28T16:30:13Z)
• A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective [24.630259061774836]
  This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Firstly, we develop a simple and reliable multi-view clustering method SCMVC that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views.
  arXiv  Detail & Related papers  (2023-09-25T09:41:11Z)
• Deep Multiview Clustering by Contrasting Cluster Assignments [14.767319805995543]
  Multiview clustering aims to reveal the underlying structure of multiview data by categorizing data samples into clusters. We propose a cross-view contrastive learning (C) method that learns view-invariant representations and produces clustering results by contrasting the cluster assignments among multiple views.
  arXiv  Detail & Related papers  (2023-04-21T06:35:54Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View [68.88732535086338]
  This paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.
  arXiv  Detail & Related papers  (2020-08-23T08:25:06Z)
• Agglomerative Neural Networks for Multi-view Clustering [109.55325971050154]
  We propose the agglomerative analysis to approximate the optimal consensus view. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly. Our evaluations against several state-of-the-art multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN.
  arXiv  Detail & Related papers  (2020-05-12T05:39:10Z)
• Generative Partial Multi-View Clustering [133.36721417531734]
  We propose a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views.
  arXiv  Detail & Related papers  (2020-03-29T17:48:27Z)

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.