Clustering Result Re-guided Incomplete Multi-view Spectral Clustering

2025-10-10

AI Summary

To address the limitations of existing spectral clustering methods for incomplete multi-view data—namely, the decoupling of feature learning and clustering, and insufficient exploitation of sample structural relationships—this paper proposes a clustering-guided joint optimization framework. Methodologically, it directly learns a nonnegative cluster indicator matrix while simultaneously modeling sample connectivity; further, it incorporates the clustering-induced Laplacian matrix into the self-representation residual term to enhance multi-view structural consistency. The algorithm unifies spectral clustering, self-representation learning, and nonnegative matrix factorization, employing multiplicative update rules for efficient iteration and providing rigorous convergence guarantees. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in clustering accuracy, robustness to missing views, and computational efficiency, thereby validating its effectiveness, stability, and theoretical soundness.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Incomplete multi-view spectral clustering generalizes spectral clustering to multi-view data and simultaneously realizes the partition of multi-view data with missing views. For this category of method, K-means algorithm needs to be performed to generate the clustering result after the procedure of feature extraction. More importantly, the connectivity of samples reflected by the clustering result is not utilized effectively. To overcome these defects, we propose Clustering Result re-Guided Incomplete Multi-view Spectral Clustering (CRG_IMSC). CRG_IMSC obtains the clustering result directly by imposing nonnegative constraint to the extracted feature. Furthermore, it constructs the connectivity matrix according to the result of spectral clustering, and minimizes the residual of self-representation based on the connectivity matrix. A novel iterative algorithm using multiplicative update is developed to solve the optimization problem of CRG_IMSC, and its convergence is proved rigorously. On benchmark datasets, for multi-view data, CRG_IMSC performs better than state-of-the-art clustering methods, and the experimental results also demonstrate the convergence of CRG_IMSC algorithm.

Problem

• Improves incomplete multi-view spectral clustering by directly obtaining clustering results
• Utilizes clustering connectivity to enhance self-representation residual minimization
• Overcomes limitations of K-means post-processing in multi-view data partitioning

Innovation

• Direct clustering via nonnegative constraint on features
• Constructing connectivity matrix from spectral clustering results
• Minimizing self-representation residual using connectivity matrix