Mixtures of spatial factor analyzers for tensor-variate data

• URL: http://arxiv.org/abs/2607.07887v1
• Date: Wed, 08 Jul 2026 19:49:46 GMT
• Title: Mixtures of spatial factor analyzers for tensor-variate data
• Abstract summary: A mixture of spatial factor analyzers (MSFA) is introduced to address the challenges of clustering high-dimensional data.<n>The proposed framework incorporates a flexible, spline-based decay covariance structure that prevents parameter inflation as dimensionality increases.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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