32 citations · 90 across the 17 of their papers we have counts for
20 papers
Parsimonious Mixtures of Skewed Bilinear Factor Analyzers
Jacob Moore, Michael P. B. Gallaugher
Mixture models which cluster skewed random matrices can often suffer from over-parameterization in the absence of performing dimension reduction. Even with the use of bilinear fact…
Model-based clustering via skewed matrix-variate cluster-weighted models
Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas +1
Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to allow the distribution of covariates to contribute to the clustering process. In a matrix-va…
Four Skewed Tensor Distributions
Michael P. B. Gallaugher, Peter A. Tait, Paul D. McNicholas
With the rise of the "big data" phenomenon in recent years, data is coming in many different complex forms. One example of this is multi-way data that come in the form of higher-or…
Multivariate Cluster Weighted Models Using Skewed Distributions
Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas +1
Much work has been done in the area of the cluster weighted model (CWM), which extends the finite mixture of regression model to include modelling of the covariates. Although many…
Skewed Distributions or Transformations? Modelling Skewness for a Cluster Analysis
Michael P. B. Gallaugher, Paul D. McNicholas, Volodymyr Melnykov +1
Because of its mathematical tractability, the Gaussian mixture model holds a special place in the literature for clustering and classification. For all its benefits, however, the G…
Mixtures of Contaminated Matrix Variate Normal Distributions
Salvatore D. Tomarchio, Michael P. B. Gallaugher, Antonio Punzo +1
Analysis of three-way data is becoming ever more prevalent in the literature, especially in the area of clustering and classification. Real data, including real three-way data, are…
Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers
Michael P. B. Gallaugher, Paul D. McNicholas
Over the years, data have become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clust…
Assessing and Visualizing Matrix Variate Normality
Nikola Pocuca, Michael P. B. Gallaugher, Katharine M. Clark +1
A framework for assessing the matrix variate normality of three-way data is developed. The framework comprises a visual method and a goodness of fit test based on the Mahalanobis s…
Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions
Utkarsh J. Dang, Michael P. B. Gallaugher, Ryan P. Browne +1
Families of mixtures of multivariate power exponential (MPE) distributions have been previously introduced and shown to be competitive for cluster analysis in comparison to other e…
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang +1
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 c…
Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers
Michael P. B. Gallaugher, Paul D. McNicholas
In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such tech…
Parameter-wise co-clustering for high-dimensional data
M. P. B. Gallaugher, C. Biernacki, P. D. McNicholas
In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-w…
Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models
Michael P. B. Gallaugher, Paul D. McNicholas
Finite mixture models have been used for unsupervised learning for some time, and their use within the semi-supervised paradigm is becoming more commonplace. Clickstream data is on…
A Mixture of Matrix Variate Bilinear Factor Analyzers
Michael P. B. Gallaugher, Paul D. McNicholas
Over the years data has become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for cluster…
On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution
Michael P. B. Gallaugher, Paul D. McNicholas
Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of weight given to the unlabelled poi…
A Matrix Variate Skew-t Distribution
Michael P. B. Gallaugher, Paul D. McNicholas
Although there is ample work in the literature dealing with skewness in the multivariate setting, there is a relative paucity of work in the matrix variate paradigm. Such work is,…
Three Skewed Matrix Variate Distributions
Michael P. B. Gallaugher, Paul D. McNicholas
Three-way data can be conveniently modelled by using matrix variate distributions. Although there has been a lot of work for the matrix variate normal distribution, there is little…
Finite Mixtures of Skewed Matrix Variate Distributions
Michael P. B. Gallaugher, Paul D. McNicholas
Clustering is the process of finding underlying group structures in data. Although mixture model-based clustering is firmly established in the multivariate case, there is a relativ…
Non-Kähler Expanding Ricci Solitons II
M. Buzano, A. S. Dancer, M. Gallaugher +1
We produce new non-Kähler, non-Einstein, complete expanding gradient Ricci solitons with conical asymptotics and underlying manifold of the form $\R^2 \times M_2 \times \cdots \tim…
A Family of Steady Ricci Solitons and Ricci-flat Metrics
M. Buzano, A. S. Dancer, M. Gallaugher +1
We produce new non-Kähler complete steady gradient Ricci solitons whose asymptotics combine those of the Bryant solitons and the Hamilton cigar. We also obtain a family of complete…