Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in rare disease cohorts from electronic health records, where data are high-dimensional but sample sizes are limited. To overcome this challenge, we incorporate a knowledge matrix extracted from a broader population that shares a partially overlapping subspace with the rare-disease cohort. Our method departs from existing approaches by relaxing restrictive one-to-one signal-alignment assumptions between the latent data matrix and knowledge matrix, allowing more flexible and realistic forms of structured sharing. We introduce a novel two-step s...
| Authors | Feiqing Huang, Zongqi Xia, Rong Ma et al. |
|---|---|
| Primary category | stat.ML |
| Published | 10 June 2026 |
| Last updated at source | 5 October 2026 |
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