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Found 4 papers, 2 papers with code
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed by including a penalty term in the likelihood constraining the parameters resulting in a flexible model for high dimensional data and a meaningful interpretation.
Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers
In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering.
Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models
A mixture of first-order continuous time Markov models is introduced for unsupervised and semi-supervised learning of clickstream data.
A Mixture of Matrix Variate Bilinear Factor Analyzers
This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification.
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