MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis

• URL: http://arxiv.org/abs/2507.02847v1
• Date: Thu, 03 Jul 2025 17:54:03 GMT
• Title: MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis
• Authors: Kunyu Zhang, Qiang Li, Shujian Yu,
• Abstract summary: MvHo-IB is a novel multi-view learning framework that integrates both pairwise interactions and HOIs for diagnostic decision-making.<n>MvHo-IB achieves state-of-the-art performance, significantly outperforming previous methods.
• Score: 22.172213148620166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent evidence suggests that modeling higher-order interactions (HOIs) in functional magnetic resonance imaging (fMRI) data can enhance the diagnostic accuracy of machine learning systems. However, effectively extracting and utilizing HOIs remains a significant challenge. In this work, we propose MvHo-IB, a novel multi-view learning framework that integrates both pairwise interactions and HOIs for diagnostic decision-making, while automatically compressing task-irrelevant redundant information. MvHo-IB introduces several key innovations: (1) a principled method that combines O-information from information theory with a matrix-based Renyi alpha-order entropy estimator to quantify and extract HOIs, (2) a purpose-built Brain3DCNN encoder to effectively utilize these interactions, and (3) a new multi-view learning information bottleneck objective to enhance representation learning. Experiments on three benchmark fMRI datasets demonstrate that MvHo-IB achieves state-of-the-art performance, significantly outperforming previous methods, including recent hypergraph-based techniques. The implementation of MvHo-IB is available at https://github.com/zky04/MvHo-IB.

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