[Paper Review] Unpaired Multi-View Graph Clustering with Cross-View Structure Matching

Yi Wen, Siwei Wang+5 more|arXiv (Cornell University)|Jul 7, 2023

Advanced Graph Neural Networks4 citations

TL;DR

This paper proposes UPMGC-SM, a parameter-free graph clustering framework for unpaired multi-view data that leverages cross-view structural matching to refine sample correspondences without prior alignment. By using a shared space and projected fixed-point optimization, it achieves robust clustering on both fully and partially unpaired datasets, outperforming existing methods in alignment accuracy and clustering performance with minimal hyperparameter tuning.

ABSTRACT

Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are pre-defined or given in advance. However, the data correspondence is often incomplete in real-world applications due to data corruption or sensor differences, referred as the data-unpaired problem (DUP) in multi-view literature. Although several attempts have been made to address the DUP issue, they suffer from the following drawbacks: 1) Most methods focus on the feature representation while ignoring the structural information of multi-view data, which is essential for clustering tasks; 2) Existing methods for partially unpaired problems rely on pre-given cross-view alignment information, resulting in their inability to handle fully unpaired problems; 3) Their inevitable parameters degrade the efficiency and applicability of the models. To tackle these issues, we propose a novel parameter-free graph clustering framework termed Unpaired Multi-view Graph Clustering framework with Cross-View Structure Matching (UPMGC-SM). Specifically, unlike the existing methods, UPMGC-SM effectively utilizes the structural information from each view to refine cross-view correspondences. Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering. Moreover, existing graph clustering methods can adopt our UPMGC-SM to enhance their ability for unpaired scenarios. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both paired and unpaired datasets.

Motivation & Objective

• To address the data-unpaired problem (DUP) in multi-view clustering, where sample correspondence between views is missing or incomplete.
• To overcome the limitations of existing methods that ignore structural information and rely on pre-defined alignment or hyperparameters.
• To develop a unified, parameter-free framework that works for both fully and partially unpaired scenarios.
• To enhance clustering performance by leveraging structural consistency across views through a shared metric space.
• To enable compatibility with any existing graph clustering method, improving their robustness in unpaired settings.

Proposed method

• Constructs individual graphs for each view using a flexible graph clustering algorithm to preserve local and global structure.
• Establishes a shared space to align cross-view data that are not in the same metric space, enabling inter-view comparison.
• Applies a projected fixed-point method with doubly stochastic projection to refine cross-view correspondences based on structural similarity.
• Uses a reconstruction loss-based view selection strategy (RCM) to identify the most informative view for alignment.
• Performs graph fusion using the refined correspondences to generate a consensus graph for clustering.
• Employs a view weighting strategy based on reconstruction loss to dynamically assign importance to different views.

Experimental results

Research questions

• RQ1 Can a parameter-free framework effectively handle both fully and partially unpaired multi-view clustering without relying on pre-defined alignment?
• RQ2 How does incorporating structural information from each view improve cross-view correspondence learning in the absence of paired data?
• RQ3 What is the impact of view selection and weighting strategies on clustering performance in unpaired settings?
• RQ4 To what extent does structural matching outperform feature-only alignment in unpaired multi-view clustering?
• RQ5 How stable and convergent is the proposed optimization framework across different datasets and unpaired ratios?

Key findings

• UPMGC-SM achieves high cross-view alignment accuracy, with MACC@10 exceeding 80% on the 3Sources dataset and remaining stable within 50 iterations.
• The framework demonstrates superior clustering performance on both paired and unpaired datasets, outperforming state-of-the-art methods in most benchmarks.
• The objective function monotonically decreases and converges within 50 iterations, indicating stable optimization.
• The view selection strategy based on minimal reconstruction loss (RCM) consistently improves performance across nine benchmark datasets.
• The learned view coefficients are non-sparse and vary across views, confirming effective integration of complementary information.
• The view weighting strategy significantly improves clustering results on most datasets, with comparable performance on others, proving its robustness and adaptability.