dorsal/arxiv
View SchemaSpatial Covariance Constraints for Gaussian Mixture Models
| Authors | Hanzhang Lu, Keiran Malott, Venkat Suprabath Bitra, Kirsty Milligan, Sanjeena Subedi, Edana Cassol, Vinita Chauhan, Connor McNairn, Bryan Muir, Prarthana Pasricha, Sangeeta Murugkar, Rowan Thomson, Andrew Jirasek, Jeffrey L. Andrews |
|---|---|
| Categories | |
| ArXiv ID | 2601.07979vv1 |
| URL | https://arxiv.org/abs/2601.07979 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Although extensive research exists in spatial modeling, few studies have addressed finite mixture model-based clustering methods for spatial data. Finite mixture models, especially Gaussian mixture models, particularly suffer from high dimensionality due to the number of free covariance parameters. This study introduces a spatial covariance constraint for Gaussian mixture models that requires only four free parameters for each component, independent of dimensionality. Using a coordinate system, the spatially constrained Gaussian mixture model enables clustering of multi-way spatial data and inference of spatial patterns. The parameter estimation is conducted by combining the expectation-maximization (EM) algorithm with the generalized least squares (GLS) estimator. Simulation studies and applications to Raman spectroscopy data are provided to demonstrate the proposed model.
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"abstract": "Although extensive research exists in spatial modeling, few studies have addressed finite mixture model-based clustering methods for spatial data. Finite mixture models, especially Gaussian mixture models, particularly suffer from high dimensionality due to the number of free covariance parameters. This study introduces a spatial covariance constraint for Gaussian mixture models that requires only four free parameters for each component, independent of dimensionality. Using a coordinate system, the spatially constrained Gaussian mixture model enables clustering of multi-way spatial data and inference of spatial patterns. The parameter estimation is conducted by combining the expectation-maximization (EM) algorithm with the generalized least squares (GLS) estimator. Simulation studies and applications to Raman spectroscopy data are provided to demonstrate the proposed model.",
"arxiv_id": "2601.07979",
"authors": [
"Hanzhang Lu",
"Keiran Malott",
"Venkat Suprabath Bitra",
"Kirsty Milligan",
"Sanjeena Subedi",
"Edana Cassol",
"Vinita Chauhan",
"Connor McNairn",
"Bryan Muir",
"Prarthana Pasricha",
"Sangeeta Murugkar",
"Rowan Thomson",
"Andrew Jirasek",
"Jeffrey L. Andrews"
],
"categories": [
"stat.ME",
"stat.ML"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Spatial Covariance Constraints for Gaussian Mixture Models",
"url": "https://arxiv.org/abs/2601.07979",
"version": "v1"
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