{
  "abstract": "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 spectral embedding procedure: first, we identify and remove irrelevant components from the knowledge matrix; then, we apply a projection-based method to separately recover shared and heterogeneous components. Simulations and an analysis of a real-world multiple sclerosis cohort show that the proposed method outperforms competing approaches, particularly in challenging scenarios where shared signals are weak and only partially aligned, as is common in rare-disease data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.11570",
  "author_total": 4,
  "authors": [
    {
      "name": "Feiqing Huang"
    },
    {
      "name": "Zongqi Xia"
    },
    {
      "name": "Rong Ma"
    },
    {
      "name": "Tianxi Cai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7mjkmsd83bvv2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.11570",
  "fulltext_url": null,
  "id": "7mjkmsd83bvv2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.11570",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7164301565",
  "pdf_url": "https://arxiv.org/pdf/2606.11570",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7mjkmsd83bvv2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:32Z",
      "native_id": "2606.11570",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.11570"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records",
  "title_text": "Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:32Z",
  "url": "https://research.draconitic.com/paper/7mjkmsd83bvv2",
  "venue": "arXiv",
  "versions": []
}