Mixtures of spatial factor analyzers for tensor-variate data
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
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.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.