用几何正则化双自编码器实现跨域表示对齐，支持新数据泛化。
Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders
- 设计几何正则化的双自编码器，保持嵌入的几何结构一致。
- 在多模态数据上提升嵌入一致性与跨域迁移能力。
- 适用于医疗诊断等单域样本少但需融合多模态信息的场景。
流形对齐（Manifold Alignment, MA）旨在跨域学习共享表示，但传统方法难以进行样本外扩展，限制了实际应用。本文提出一种基于几何正则化双自编码器（geometry-regularized twin autoencoder）的引导式表示学习框架，增强流形对齐并支持对未见数据的泛化。通过引入预训练对齐模型和多任务学习机制，该方法在保持嵌入几何保真度的同时，提升了跨域泛化能力和表示鲁棒性。在多个基准上验证，其嵌入一致性、信息保留度及跨域迁移性能均优于现有方法。进一步应用于阿尔茨海默病诊断，成功整合多模态患者数据，在仅依赖单一模态的场景下，借助多模态知识显著提升预测准确性。
原文摘要 · Abstract (English)
Manifold alignment (MA) involves a set of techniques for learning shared representations across domains, yet many traditional MA methods are incapable of performing out-of-sample extension, limiting their real-world applicability. We propose a guided representation learning framework leveraging a geometry-regularized twin autoencoder (AE) architecture to enhance MA while enabling generalization to unseen data. Our method enforces structured cross-modal mappings to maintain geometric fidelity in learned embeddings. By incorporating a pre-trained alignment model and a multitask learning formulation, we improve cross-domain generalization and representation robustness while maintaining alignment fidelity. We evaluate our approach using several MA methods, showing improvements in embedding consistency, information preservation, and cross-domain transfer. Additionally, we apply our framework to Alzheimer's disease diagnosis, demonstrating its ability to integrate multi-modal patient data and enhance predictive accuracy in cases limited to a single domain by leveraging insights from the multi-modal problem.
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