Work

Learning Multi-View Representations with Graph-Level Fusion: A Bi-Level Optimization Perspective

Year: 2026

Type: article

Abstract: Multi-view representation learning, aiming to extract discriminative patterns from heterogeneous data sources, has attracted increasing attention in recent years. A central challenge in multi-view data fusion lies in determining the appropriate weight and importance of each view in a principled manner. While graph-based methodologies have proven effective for multi-view learning, the systematic development of optimization-centric methodologies for consistent graph fusion remains in its nascent stages. Furthermore, the lack of explicit alignment between the latent feature representation space and the graph space is frequently overlooked, ultimately compromising the consistency of the shared representation. To this end, we propose a bi-level framework for joint multi-view feature representation learning and graph fusion, which formally formulates the multi-view data fusion task as a rigorous mathematical optimization problem. The proposed approach demonstrates consistent effectiveness in clustering tasks, achieving state-of-the-art performance on six benchmark datasets with accuracy improvements ranging up to 9.73% over existing methods.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10090289

Authors: Na Song, Xin Wang, Zhicai Zhang, Shiping Wang

Institutions: Putian University, Fuzhou University, Hainan University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hainan Province