Multi-view subspace clustering algorithm based on low-rank enhancement

Zhang Tianxiao; Zhang Yong; Liu Wenzhe

doi:10.11959/j.issn.1000-0801.2026007

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Multi-view subspace clustering algorithm based on low-rank enhancement

Research and Development | 更新时间：2026-04-08

- Multi-view subspace clustering algorithm based on low-rank enhancement
- Telecommunications Science Vol. 42, Issue 3, Pages: 81-96(2026)
- 作者机构：
  
  1.湖州师范学院信息工程学院，浙江湖州 313000
  2.辽宁师范大学计算机与人工智能学院，辽宁大连 116033
- 作者简介：
- 基金信息：
  
  the Scientific Research Fund of Zhejiang Provincial Education Department under Gr(Y202456367;Y202456437);The Huzhou Natural Science Foundation(2022GZ08;2023ZD2004;2024YZ39)
- DOI：10.11959/j.issn.1000-0801.2026007
  CLC： TP301.6
- Received：09 May 2025，
  
  Revised：2025-06-10，
  
  Accepted：24 June 2025，
  
  Published：20 March 2026
- 稿件说明：
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张天啸,张永,刘文哲.基于增强低秩的多视图子空间聚类算法[J].电信科学,2026,42(03):81-96.

Zhang Tianxiao,Zhang Yong,Liu Wenzhe.Multi-view subspace clustering algorithm based on low-rank enhancement[J].Telecommunications Science,2026,42(03):81-96.
张天啸,张永,刘文哲.基于增强低秩的多视图子空间聚类算法[J].电信科学,2026,42(03):81-96. DOI： 10.11959/j.issn.1000-0801.2026007.

Zhang Tianxiao,Zhang Yong,Liu Wenzhe.Multi-view subspace clustering algorithm based on low-rank enhancement[J].Telecommunications Science,2026,42(03):81-96. DOI： 10.11959/j.issn.1000-0801.2026007.

摘要

多视图子空间聚类（multi-view subspace clustering

MVSC）因其能够有效融合多源异构数据而被广泛应用，但其仍面临两大挑战：噪声对亲和矩阵鲁棒性的影响以及捕获视图间一致性信息能力不足。为此，提出了一种融合双重去噪与成对相似性思想的MVSC算法。首先，该算法在数据处理初期采用双重去噪策略，通过低通滤波器实现平滑去噪，继而构建多步矩阵分解框架以实现低秩化去噪；其次，引入成对相似性思想，通过构建视图间一致性约束来有效挖掘视图间的一致性信息；最后，采用基于增广拉格朗日乘子法（augmented lagrange method，ALM）求解目标函数。该算法在7个不同领域数据集上均表现出显著优势，与其他算法相比，该算法在准确度、归一化互信息和F-score指标上分别平均提升了17.79%、20.72%和16.37%。其中CESC数据集的准确度达到了0.862 9，较高效、有效的一步式多视图聚类（efficient and effective one-step multiview clustering，EEOMVC）方法提升了16.13%，充分体现了该算法在多视图数据融合方面的优越性能。

Abstract

Multi-view subspace clustering (MVSC) is widely recognized for its effectiveness in integrating multi-source heterogeneous data

yet two critical challenges are identified: insufficient robustness of affinity matrices against noise and limited capability in capturing cross-view consistent information. To address these issues

a multi-view subspace clustering algorithm was proposed by integrating dual denoising mechanisms with pairwise similarity principles. Firstly

a dual denoising strategy was designed during the initial data processing stage

where smooth denoising was achieved through low-pass filters

followed by low-rank denoising via a multi-step matrix decomposition framework. Subsequently

pairwise similarity principles were introduced

and inter-view consistency constraints were systematically constructed to effectively explore shared information across views. Finally

an optimization framework was developed using the augmented Lagrangian method (ALM) . The proposed algorithm demonstrates significant advantages across seven datasets from different domains. Compared with other algorithms

it achieves average improvements of 17.79% in accuracy

20.72% in normalized mutual information

and 16.37% in F-score metrics. Particularly on the CESC dataset

the algorithm attains an ACC score of 0.862 9

representing a 16.13% improvement over the efficient and effective one-step multiview clustering (EEOMVC)

which fully demonstrates its superior performance in multi-view data fusion.

关键词

Keywords

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