Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering

2025-01-21

AI Summary

To address three key challenges in incomplete multi-view clustering—view incompleteness, rotation sensitivity of spectral embeddings, and computational inefficiency for large-scale data—this paper proposes the Rotation-Invariant Spectral Embedding (RISE) method. RISE jointly learns view-specific embeddings and recovers a second-order rotation-invariant consensus spectral representation within a unified framework. It models missing-view structures via an incomplete bipartite graph and devises a linear-complexity alternating optimization algorithm for scalable solution. Theoretically grounded in spectral analysis and empirically validated, RISE achieves O(n) time complexity while maintaining interpretability and robustness. Extensive experiments on multiple benchmark datasets demonstrate that RISE significantly outperforms state-of-the-art methods in clustering accuracy, robustness to missing views, and scalability.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as these methods often ignore guidance from spectral analysis; (2) Complex optimization processes require high computational burden, hindering scalability to large-scale problems; (3) Most methods do not address the rotational mismatch problem in spectral embeddings. To address these issues, we propose a highly efficient rotation-invariant spectral embedding (RISE) method for scalable incomplete multi-view clustering. RISE learns view-specific embeddings from incomplete bipartite graphs to capture the complementary information. Meanwhile, a complete consensus representation with second-order rotation-invariant property is recovered from these incomplete embeddings in a unified model. Moreover, we design a fast alternating optimization algorithm with linear complexity and promising convergence to solve the proposed formulation. Extensive experiments on multiple datasets demonstrate the effectiveness, scalability, and efficiency of RISE compared to the state-of-the-art methods.

Problem

• Multi-view Clustering
• Incomplete View Handling
• Large-scale Dataset

Innovation

• RISE
• Multi-view Incomplete Data Clustering
• Rotation Invariance