Ryan P. Browne
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View article: Protocol for development of a checklist and guideline for transparent reporting of cluster analyses (TRoCA)
Protocol for development of a checklist and guideline for transparent reporting of cluster analyses (TRoCA) Open
Introduction Cluster analysis, a machine learning-based and data-driven technique for identifying groups in data, has demonstrated its potential in a wide range of contexts. However, critical appraisal and reproducibility are often limited…
View article: Model-based bi-clustering using multivariate Poisson-lognormal with general block-diagonal covariance matrix and its applications
Model-based bi-clustering using multivariate Poisson-lognormal with general block-diagonal covariance matrix and its applications Open
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 researched. A multivariate Poisson-lognormal (MPLN) model-base…
View article: Statistical inference for sketching algorithms
Statistical inference for sketching algorithms Open
Sketching algorithms use random projections to generate a smaller sketched data set, often for the purposes of modelling. Complete and partial sketch regression estimates can be constructed using information from only the sketched data set…
View article: Statistical inference for sketching algorithms
Statistical inference for sketching algorithms Open
Sketching algorithms use random projections to generate a smaller sketched data set, often for the purposes of modelling. Complete and partial sketch regression estimates can be constructed using information from only the sketched data set…
View article: Estimation of Gaussian Bi-Clusters with General Block-Diagonal Covariance Matrix and Applications
Estimation of Gaussian Bi-Clusters with General Block-Diagonal Covariance Matrix and Applications Open
Bi-clustering is a technique that allows for the simultaneous clustering of observations and features in a dataset. This technique is often used in bioinformatics, text mining, and time series analysis. An important advantage of biclusteri…
View article: Assessing the variability of posterior probabilities in Gaussian model-based clustering
Assessing the variability of posterior probabilities in Gaussian model-based clustering Open
We propose a variant of the bootstrap to assess the variability of posterior probabilities arising from Gaussian model-based clustering. The bootstrap variant uses predictions based on out-of-bootstrap-sample observations and then construc…
View article: One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes Open
Increasingly large datasets are rapidly driving up the computational costs of machine learning. Prototype generation methods aim to create a small set of synthetic observations that accurately represent a training dataset but greatly reduc…
View article: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: <b>MixGHD</b> <i>R</i> package
Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: <b>MixGHD</b> <i>R</i> package Open
The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivar…
View article: A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data
A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data Open
Multivariate count data are commonly encountered through high-throughput sequencing technologies in bioinformatics, text mining, or in sports analytics. Although the Poisson distribution seems a natural fit to these count data, its multiva…
View article: A parsimonious family of multivariate Poisson-lognormal distributions\n for clustering multivariate count data
A parsimonious family of multivariate Poisson-lognormal distributions\n for clustering multivariate count data Open
Multivariate count data are commonly encountered through high-throughput\nsequencing technologies in bioinformatics, text mining, or in sports analytics.\nAlthough the Poisson distribution seems a natural fit to these count data, its\nmult…
View article: Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions
Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions Open
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 elliptical mixtures including mixtures of Gaussian distribut…
View article: Flexible clustering of high‐dimensional data via mixtures of joint generalized hyperbolic distributions
Flexible clustering of high‐dimensional data via mixtures of joint generalized hyperbolic distributions Open
A mixture of joint generalized hyperbolic distributions (MJGHD) is introduced for asymmetric clustering for high‐dimensional data. The MJGHD approach takes into account the cluster‐specific subspaces, thereby limiting the number of paramet…
View article: Asymmetric Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Models
Asymmetric Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Models Open
A mixture of joint generalized hyperbolic models is introduced for asymmetric clustering for high-dimensional data (MJGHM-HDClust). The MJGHM-HDClust approach takes into account the cluster specific subspace and therefore limits the number…