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Efficient spectral unmixing using endmember variability — arXiv:2610.23793

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.23793
License (as archived): CC0-1.0
Archived: 2026-10-19T09:37:40.472Z
SHA-256 of the archived text: c360caeaba60938b671a87a934e8e8f7b50b38f3e1ab4905a24301b9d55e6f92
Stable URL of this page: https://metraipolis.com/documento/dc_7643dbff46de

How to cite

Efficient spectral unmixing using endmember variability — arXiv:2610.23793 — arXiv (en), https://arxiv.org/abs/2610.23793 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7643dbff46de (Metraipolis archive dc_7643dbff46de, sha256 c360caeaba60938b671a87a934e8e8f7b50b38f3e1ab4905a24301b9d55e6f92)

BibTeX

@misc{dc7643dbff46de,
  title = {Efficient spectral unmixing using endmember variability — arXiv:2610.23793},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.23793},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7643dbff46de; sha256 c360caeaba60938b671a87a934e8e8f7b50b38f3e1ab4905a24301b9d55e6f92},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.23793. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A mixed signal vector composed of a weighted sum of random, unobserved endmember vectors has a mean and a variance that both depend on the unknown weights. This suggests that linear estimates of the weights based on the mean model are likely to be inefficient, and could possibly be improved upon by estimates that also make use of the variance model. In the context of a latent factor model for source variability, we calculate theoretically how much information is available in the variance model, and show how this information may stabilize inference in the case of an ill-conditioned mean model. We show through several data examples that utilization of the variance model is likely to improve parameter estimation in real-world scenarios.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2610.23793 — 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 2026-10-19T09:32:59.745Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).