4 papers
Mixtures of spatial factor analyzers for tensor-variate data
Hanzhang Lu, Keiran Malott, Kirsty Milligan +9
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, th…
Spatial Covariance Constraints for Gaussian Mixture Models
Hanzhang Lu, Keiran Malott, Venkat Suprabath Bitra +11
Although extensive research exists in spatial modeling, few studies have addressed finite mixture model-based clustering methods for spatial data. Finite mixture models, especially…
Model-based bi-clustering using multivariate Poisson-lognormal with general block-diagonal covariance matrix and its applications
Caitlin Kral, Evan Chance, Ryan Browne +1
While several Gaussian mixture models-based biclustering approaches currently exist in the literature for continuous data, approaches to handle discrete data have not been well res…
Extending Cluster-Weighted Factor Analyzers for multivariate prediction and high-dimensional interpretability
Xiaoke Qin, Francesca Martella, Sanjeena Subedi
Cluster-weighted factor analyzers (CWFA) are a versatile class of mixture models designed to estimate the joint distribution of a random vector that includes a response variable al…