{
  "abstract": "A mixture of spatial factor analyzers (MSFA) is introduced to address the challenges of clustering high-dimensional spatial data. The proposed framework utilizes the given coordinate system to implement a flexible, spline-based spatial decay covariance structure. In this approach, the number of covariance parameters increases linearly with the number of spatial locations, rather than increasing quadratically. To model non-spatial dependence, matrix variate factor analyzers are employed for further dimensionality reduction. Parameter estimation is conducted via a variant of the expectation-maximization algorithm combined with a generalized least squares estimator. The proposed models are explored in the context of tensor-variate data analysis, where simulation studies and three applications---Raman spectroscopy, SpecTex hyperspectral textures, and the Salinas hyperspectral scene---illustrate their ability to recover and distinguish spatial dependence patterns. The R implementation is available on CRAN: https://CRAN.R-project.org/package=SpatMix.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.07887",
  "author_total": 12,
  "authors": [
    {
      "name": "Hanzhang Lu"
    },
    {
      "name": "Keiran Malott"
    },
    {
      "name": "Kirsty Milligan"
    },
    {
      "name": "Sanjeena Subedi"
    },
    {
      "name": "Edana Cassol"
    },
    {
      "name": "Vinita Chauhan"
    },
    {
      "name": "Connor McNairn"
    },
    {
      "name": "Prarthana Pasricha"
    },
    {
      "name": "Sangeeta Murugkar"
    },
    {
      "name": "Rowan M. Thomson"
    },
    {
      "name": "Andrew Jirasek"
    },
    {
      "name": "Jeffrey L. Andrews"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1n1gkxtzysrj9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.07887",
  "fulltext_url": null,
  "id": "1n1gkxtzysrj9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.07887",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Bayesian Methods and Mixture Models"
    ]
  ],
  "openalex": "W7167912833",
  "pdf_url": "https://arxiv.org/pdf/2607.07887",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1n1gkxtzysrj9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2607.07887",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.07887"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Mixtures of spatial factor analyzers for tensor-variate data",
  "title_text": "Mixtures of spatial factor analyzers for tensor-variate data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/1n1gkxtzysrj9",
  "venue": "arXiv",
  "versions": []
}