Missing Pattern Tree based Decision Grouping and Ensemble for Deep Incomplete Multi-View Clustering

2025-12-25

AI Summary

Real-world multi-view data often exhibit highly heterogeneous missing patterns, severely limiting the effectiveness of existing incomplete multi-view clustering (IMVC) methods in exploiting available view pairs. To address this, we propose a missing-pattern-tree-driven grouped clustering framework: first, a missing-pattern tree is constructed to hierarchically group samples based on their missingness structures; then, within each group, multi-view subspace learning is performed, enhanced by two synergistic mechanisms—uncertainty-aware weighted ensemble and ensemble-to-individual knowledge distillation—to improve cross-group consistency and individual discriminability. Our key contributions are: (1) the first principled modeling of missing patterns via a tree-based structure with decision-guided sample grouping; and (2) a novel joint optimization paradigm integrating uncertainty-aware ensemble weighting and cross-view knowledge distillation. Extensive experiments on multiple benchmark datasets demonstrate state-of-the-art performance, significantly improving robustness and stability under highly heterogeneous missingness scenarios.

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

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Real-world multi-view data usually exhibits highly inconsistent missing patterns which challenges the effectiveness of incomplete multi-view clustering (IMVC). Although existing IMVC methods have made progress from both imputation-based and imputation-free routes, they have overlooked the pair under-utilization issue, i.e., inconsistent missing patterns make the incomplete but available multi-view pairs unable to be fully utilized, thereby limiting the model performance. To address this, we propose a novel missing-pattern tree based IMVC framework entitled TreeEIC. Specifically, to achieve full exploitation of available multi-view pairs, TreeEIC first defines the missing-pattern tree model to group data into multiple decision sets according to different missing patterns, and then performs multi-view clustering within each set. Furthermore, a multi-view decision ensemble module is proposed to aggregate clustering results from all decision sets, which infers uncertainty-based weights to suppress unreliable clustering decisions and produce robust decisions. Finally, an ensemble-to-individual knowledge distillation module transfers the ensemble knowledge to view-specific clustering models, which enables ensemble and individual modules to promote each other by optimizing cross-view consistency and inter-cluster discrimination losses. Extensive experiments on multiple benchmark datasets demonstrate that our TreeEIC achieves state-of-the-art IMVC performance and exhibits superior robustness under highly inconsistent missing patterns.

Problem

• Addresses under-utilization of incomplete multi-view pairs in clustering
• Groups data by missing patterns to fully exploit available information
• Ensembles and distills clustering decisions for robust performance

Innovation

• Missing-pattern tree groups data by missing patterns
• Multi-view decision ensemble suppresses unreliable clustering decisions
• Ensemble-to-individual knowledge distillation transfers ensemble knowledge