Sparse model-based clustering of three-way data via lasso-type penalties
Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering
continuous matrix-variate data, which are increasingly common in various fields. Despite …
continuous matrix-variate data, which are increasingly common in various fields. Despite …
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
AA Sochaniwsky, MPB Gallaugher, Y Tang… - Journal of …, 2024 - Springer
Robust clustering of high-dimensional data is an important topic because clusters in real
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …
Model-based clustering for covariance matrices via penalized Wishart mixture models
Covariance matrices provide a valuable source of information about complex interactions
and dependencies within the data. However, from a clustering perspective, this information …
and dependencies within the data. However, from a clustering perspective, this information …
The parsimonious Gaussian mixture models with partitioned parameters and their application in clustering
NAA Olyaei, M Khazaei, D Najarzadeh - Statistical Methods & Applications, 2024 - Springer
Cluster analysis is a method that identifies similar groups of data without any prior
knowledge of the relevant groups. One of the most widely used clustering methods is model …
knowledge of the relevant groups. One of the most widely used clustering methods is model …
Flexible clustering with a sparse mixture of generalized hyperbolic distributions
AA Sochaniwsky, MPB Gallaugher, Y Tang… - arxiv preprint arxiv …, 2019 - arxiv.org
Robust clustering of high-dimensional data is an important topic because clusters in real
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …
A model-based approach to cluster correlation matrices from fMRI signals via a mixture of sparse Wishart distributions
E BORRINI - 2023 - politesi.polimi.it
Abstract Functional Magnetic Resonance Imaging (fMRI) has revolutionized our ability to
observe the human brain in action, capturing intricate spatiotemporal patterns of brain …
observe the human brain in action, capturing intricate spatiotemporal patterns of brain …
Robust model-based clustering for high-dimensional data via covariance matrices regularization
D Zaltieri, L Panzeri - 2022 - politesi.polimi.it
Robust clustering for high-dimensional data poses a significant challenge, as existing robust
clustering methods suffer from the curse of dimensionality when p is large, while existing …
clustering methods suffer from the curse of dimensionality when p is large, while existing …