Parsimonious Gaussian mixture models

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Model-based clustering of high-dimensional data: A review (2014, methodology): “A general framework for the MFA model was also proposed by McNicholas & Murphy [69] which, in particular, includes the previous works of Ghahramani and Hinton and of McLachlan et al. [65].”
A tutorial on task-parameterized movement learning and retrieval (2015, methodology): “For each approach, a dedicated EM update can be derived corresponding to the type of constraints considered [61].”
Model-Based Clustering (2016, background): “A greater level of parsimony can be attained by constraining the component factor loading matrices to be equal, i.e., Λg = Λ. McNicholas and Murphy (2005, 2008) develop a family of eight parsimonious Gaussian mixture models (PGMMs) for clustering by imposing, or not, each of the constraints Λg = Λ,…”