Multi-View Learning and Applications

Dear Colleagues,

With the rapid growth of multimodal data in today’s digital ecosystem, multi-view learning has become a powerful paradigm for integrating heterogeneous information sources to achieve more comprehensive, robust, and insightful data understanding. By leveraging complementary information from multiple views—such as images, text, audio, sensor streams, social interactions, or geometric structures—multi-view learning offers enhanced representational capacity and significantly improves performance in complex machine learning tasks. These capabilities have driven substantial progress across diverse domains, including computer vision, autonomous systems, healthcare informatics, cybersecurity, remote sensing, and industrial intelligence.

As multi-view data grow in volume, diversity, and complexity, advancing effective algorithms capable of aligning, fusing, and reasoning across multiple perspectives has become increasingly crucial. Challenges such as view inconsistency, missing or noisy views, large-scale view representation, dynamic view generation, and cross-view semantic understanding remain open research frontiers. Furthermore, with the emergence of foundation models and their integration with multimodal and multi-view signals, new opportunities arise for unified representation learning, knowledge transfer, and scalable cross-domain applications.

This Special Issue aims to provide a forum for researchers, engineers, and practitioners to share recent advances, explore fundamental challenges, and discuss future directions in multi-view learning and its broad range of real-world applications. We welcome original research articles, reviews, and application-oriented studies that develop new theories, models, algorithms, or systems to advance the capabilities and impact of multi-view learning.

Topics of interest include, but are not limited to, the following:

• Multi-view representation learning and feature fusion;
• Multi-view clustering, classification, and retrieval;
• Cross-view alignment, matching, and semantic understanding;
• Multi-view learning for multimodal large models;
• Robust multi-view learning under noise, sparsity, or missing data;
• Multi-view learning in computer vision, robotics, and autonomous driving;
• Multi-view approaches in bioinformatics, healthcare, smart city, IoT, cybersecurity, and industry;
• Benchmarks, datasets, and evaluation protocols for multi-view research.

Dr. Chang Tang
Dr. Chunwei Tian
Dr. Weiqing Yan
Dr. Sen Xiang
Guest Editors

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• multi-view representation learning
• cross-view feature fusion
• multimodal data integration
• cross-view alignment and matching
• multi-view applications in AI systems