Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912609609973760 |
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| author | Rhodes, Jake S. Rustad, Adam G. Nielsen, Marshall S. McClellan, Morgan Chase Gardner, Dallan Hedges, Dawson |
| author_facet | Rhodes, Jake S. Rustad, Adam G. Nielsen, Marshall S. McClellan, Morgan Chase Gardner, Dallan Hedges, Dawson |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders Rhodes, Jake S. Rustad, Adam G. Nielsen, Marshall S. McClellan, Morgan Chase Gardner, Dallan Hedges, Dawson Machine Learning 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. |
| title | Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.22913 |