Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Institution: Okanagan University College
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2102128917, G2271590008, G2614460161, G5266515567, Model-based clustering of high dimensional discrete data and compositional data, Topics in unsupervised statistical learning, Development of optical spectroscopic and 3D dosimetric systems in radiation therapy, G8245661358, Raman spectroscopy and imaging techniques for ionizing radiation research
OpenAlex ID: W7167912833
Cites:
Cited by:
Related to: