Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering

• URL: http://arxiv.org/abs/2604.18024v1
• Date: Mon, 20 Apr 2026 09:49:42 GMT
• Title: Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering
• Abstract summary: We study the problem of pre-clustering noisy-view analysis in multi-view data from a clusterability perspective.<n>We propose a Multi-View Clusterability Score (MVCS), which quantifies the strength of latent cluster-related structures in multi-view data.<n>We further use it to perform potentially noisy view analysis and noisy-view detection before clustering.
• Score: 14.310871812932193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-view clustering, the quality of different views may vary substantially, and low-quality or degraded views can impair overall clustering performance. However, existing studies mainly address this issue within the clustering process through view weighting or noise-robust optimization, while paying limited attention to data-level assessment before clustering. In this paper, we study the problem of pre-clustering noisy-view analysis in multi-view data from a clusterability perspective. To this end, we propose a Multi-View Clusterability Score (MVCS), which quantifies the strength of latent cluster-related structures in multi-view data through three complementary components: per-view structural clusterability, joint-space clusterability, and cross-view neighborhood consistency. To the best of our knowledge, this is the first clusterability score specifically designed for multi-view data. We further use it to perform potentially noisy view analysis and noisy-view detection before clustering. Extensive experiments on real-world datasets demonstrate that noisy views can significantly degrade clustering performance, and that, compared with existing clusterability measures designed for single-view data, the proposed method more effectively supports noisy-view analysis and detection.

Related papers

• Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment [15.396375506151102]
  We propose a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components.<n> Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.
  arXiv  Detail & Related papers  (2026-01-14T00:46:00Z)
• Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence [72.41989962665285]
  Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views.<n>However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views.<n>We design a causal multi-view clustering network, termed CauMVC, to tackle this problem.
  arXiv  Detail & Related papers  (2025-09-19T14:31:40Z)
• Structure-guided Deep Multi-View Clustering [13.593229506936682]
  Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance.<n>Most of the existing clustering methods often neglect to fully mine multi-view structural information.<n>We propose a structure-guided deep multi-view clustering model to explore the distribution of multi-view data.
  arXiv  Detail & Related papers  (2025-01-17T12:42:30Z)
• 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.<n>The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information.<n>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)
• 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)
• 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)

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.