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Fast Functional Clustering via Closed-Form Covariance Updates — arXiv:2611.08227

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08227
License (as archived): CC0-1.0
Archived: 2026-11-06T14:51:53.529Z
SHA-256 of the archived text: 072255bc7076122b3b66ff7dd10651b5837e39a7b37d2cf2f3f83e803d3c2339
Stable URL of this page: https://metraipolis.com/documento/dc_bbfece72542e

How to cite

Fast Functional Clustering via Closed-Form Covariance Updates — arXiv:2611.08227 — arXiv (en), https://arxiv.org/abs/2611.08227 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bbfece72542e (Metraipolis archive dc_bbfece72542e, sha256 072255bc7076122b3b66ff7dd10651b5837e39a7b37d2cf2f3f83e803d3c2339)

BibTeX

@misc{dcbbfece72542e,
  title = {Fast Functional Clustering via Closed-Form Covariance Updates — arXiv:2611.08227},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08227},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bbfece72542e; sha256 072255bc7076122b3b66ff7dd10651b5837e39a7b37d2cf2f3f83e803d3c2339},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08227. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.AP, nuovi lavori del giorno).
Categorie arXiv: Computation (stat.CO) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2611.08227 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:22:57.501Z dalla pagina https://arxiv.org/list/stat.AP/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).