{
  "abstract": "Functional clustering identifies coordinated molecular profiles, but repeated numerical covariance optimization can make mixture-model fitting computationally demanding. We develop a fast functional clustering procedure for joint analysis across conditions, combining linear mean profiles on a standardized log niche coordinate with first-order structured antedependence (SAD(1)) covariance. Weighted residual statistics yield closed-form conditional covariance updates under four parameter-sharing specifications, while an innovation representation simplifies likelihood evaluation. In same-R comparisons with numerical optimization of identical conditional objectives, the closed-form solver was 91.5--201.4 times as fast, with objective agreement in all 60 targets. Under matched complete mixture fits, it achieved a 1.87--2.37-fold speedup with identical cluster assignments. Recovery simulations showed higher module classification accuracy and lower mean-curve error than $K$-means and a Gaussian mixture with shared full covariance when the mean structure was correctly specified. SAD(1) provided an additional recovery gain under strong residual correlation, whereas curved-mean controls identified the importance of an appropriate mean family. Applications to single-condition gut microbiota, paired leaf--root microbiota and three-visit human gut multi-omics illustrate the framework across different numbers of conditions. Closed-form covariance estimation makes joint functional clustering substantially faster while retaining the fitted model's likelihood and assignments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08227",
  "author_total": 5,
  "authors": [
    {
      "name": "Yu Wang"
    },
    {
      "name": "Jiaze Guo"
    },
    {
      "name": "Yao Chen"
    },
    {
      "name": "Tianxiang Hao"
    },
    {
      "name": "Rongling Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ankws13pwpp69.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08227",
  "fulltext_url": null,
  "id": "ankws13pwpp69",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08227",
  "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",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.CO"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08227",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ankws13pwpp69.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:40Z",
      "native_id": "2611.08227",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08227"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Fast Functional Clustering via Closed-Form Covariance Updates",
  "title_text": "Fast Functional Clustering via Closed-Form Covariance Updates",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:52Z",
  "url": "https://research.draconitic.com/paper/ankws13pwpp69",
  "venue": "arXiv",
  "versions": []
}