{"ID":6267756,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-11T19:41:01.577799096Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07887","arxiv_id":"2607.07887","title":"Mixtures of spatial factor analyzers for tensor-variate data","abstract":"A mixture of spatial factor analyzers (MSFA) is introduced to address the challenges of clustering high-dimensional spatial data. By leveraging the underlying coordinate system, the proposed framework incorporates a flexible, spline-based spatial decay covariance structure that prevents parameter inflation as dimensionality increases. 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 applications to Raman spectroscopy and hyperspectral texture databases demonstrate their capacity to accurately infer and differentiate distinct spatial patterns.","short_abstract":"A mixture of spatial factor analyzers (MSFA) is introduced to address the challenges of clustering high-dimensional spatial data. By leveraging the underlying coordinate system, the proposed framework incorporates a flexible, spline-based spatial decay covariance structure that prevents parameter inflation as dimension...","url_abs":"https://arxiv.org/abs/2607.07887","url_pdf":"https://arxiv.org/pdf/2607.07887v1","authors":"[\"Hanzhang Lu\",\"Keiran Malott\",\"Kirsty Milligan\",\"Sanjeena Subedi\",\"Edana Cassol\",\"Vinita Chauhan\",\"Connor McNairn\",\"Prarthana Pasricha\",\"Sangeeta Murugkar\",\"Rowan Thomson\",\"Andrew Jirasek\",\"Jeffrey L. Andrews\"]","published":"2026-07-08T19:49:46Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}