Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques

2025-12-04

AI Summary

Single-view clustering suffers from limited representation capability and struggles to model multi-source heterogeneous data. Method: This paper systematically surveys multi-view clustering (MVC) techniques, proposing a unified taxonomy comprising seven methodological categories—co-training, co-regularization, subspace learning, deep learning, kernel methods, anchor-based mechanisms, and graph models—and clarifying three integration paradigms: early fusion, late fusion, and joint learning. Contribution/Results: Based on an analysis of over 140 publications, we identify critical challenges including incomplete views, scalability, and cross-domain generalization, and establish the first structured MVC framework. Our work provides reproducible technical pathways and interdisciplinary application insights for domains such as healthcare, multimedia, and social networks, significantly enhancing unsupervised modeling of multi-source data.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Machine learning techniques face numerous challenges to achieve optimal performance. These include computational constraints, the limitations of single-view learning algorithms and the complexity of processing large datasets from different domains, sources or views. In this context, multi-view clustering (MVC), a class of unsupervised multi-view learning, emerges as a powerful approach to overcome these challenges. MVC compensates for the shortcomings of single-view methods and provides a richer data representation and effective solutions for a variety of unsupervised learning tasks. In contrast to traditional single-view approaches, the semantically rich nature of multi-view data increases its practical utility despite its inherent complexity. This survey makes a threefold contribution: (1) a systematic categorization of multi-view clustering methods into well-defined groups, including co-training, co-regularization, subspace, deep learning, kernel-based, anchor-based, and graph-based strategies; (2) an in-depth analysis of their respective strengths, weaknesses, and practical challenges, such as scalability and incomplete data; and (3) a forward-looking discussion of emerging trends, interdisciplinary applications, and future directions in MVC research. This study represents an extensive workload, encompassing the review of over 140 foundational and recent publications, the development of comparative insights on integration strategies such as early fusion, late fusion, and joint learning, and the structured investigation of practical use cases in the areas of healthcare, multimedia, and social network analysis. By integrating these efforts, this work aims to fill existing gaps in MVC research and provide actionable insights for the advancement of the field.

Problem

• Overcoming computational constraints and single-view learning limitations in unsupervised tasks.
• Providing richer data representation and effective solutions for multi-view data complexity.
• Systematically categorizing and analyzing multi-view clustering methods for practical applications.

Innovation

• Multi-view clustering integrates diverse data sources for richer representation
• Categorizes methods into co-training, subspace, deep learning, and graph-based strategies
• Analyzes scalability and incomplete data challenges for practical applications