128 citations · 237 across the 19 of their papers we have counts for
19 papers
Data compression for fast dimension reduction and clustering of high-dimensional discrete data
Silvia D'Angelo, Michael Fop
High-dimensional discrete data are common in genomics, microbiomics, survey research, and digital behavioral analysis. Clustering such data is challenging because many existing met…
A latent variable model for identifying and characterizing food adulteration
Alessandro Casa, Thomas Brendan Murphy, Michael Fop
Recently, growing consumer awareness of food quality and sustainability has led to a rising demand for effective food authentication methods. Vibrational spectroscopy techniques ha…
A Latent Position Co-Clustering Model for Multiplex Networks
C. J. Clarke, Michael Fop
Multiplex networks are increasingly common across diverse domains, motivating the development of clustering methods that uncover patterns at multiple levels. Existing approaches ty…
A Gaussian process approach for rapid evaluation of skin tension
Matt Nagle, Hannah Conroy Broderick, Christelle Vedel +3
Skin tension plays a pivotal role in clinical settings, it affects scarring, wound healing and skin necrosis. Despite its importance, there is no widely accepted method for assessi…
Analysis of in-vivo skin anisotropy using elastic wave measurements and Bayesian modelling
Matt Nagle, Susan Price, Antonia Trotta +3
In vivo skin exhibits viscoelastic, hyper-elastic and non-linear characteristics. It is under a constant non-equibiaxial tension in its natural configuration and is reinforced with…
Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks
Iuliia Promskaia, Adrian O'Hagan, Michael Fop
Network data often represent multiple types of relations, which can also denote exchanged quantities, and are typically encompassed in a weighted multiplex. Such data frequently ex…
A Dirichlet stochastic block model for composition-weighted networks
Iuliia Promskaia, Adrian O'Hagan, Michael Fop
Network data are observed in various applications where the individual entities of the system interact with or are connected to each other, and often these interactions are defined…
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
Ganesh Babu, Aoife Gowen, Michael Fop +1
The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to…
Variational Inference for the Latent Shrinkage Position Model
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
The latent position model (LPM) is a popular method used in network data analysis where nodes are assumed to be positioned in a -dimensional latent space. The latent shrinkage p…
Model-based Clustering for Network Data via a Latent Shrinkage Position Cluster Model
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assum…
Sparse model-based clustering of three-way data via lasso-type penalties
Andrea Cappozzo, Alessandro Casa, Michael Fop
Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are becoming increasingly prevalent in various fiel…
A Latent Shrinkage Position Model for Binary and Count Network Data
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
Interactions between actors are frequently represented using a network. The latent position model is widely used for analysing network data, whereby each actor is positioned in a l…
Group-wise shrinkage estimation in penalized model-based clustering
Alessandro Casa, Andrea Cappozzo, Michael Fop
Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these…
Unobserved classes and extra variables in high-dimensional discriminant analysis
Michael Fop, Pierre-Alexandre Mattei, Charles Bouveyron +1
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may b…
Model-based clustering for multidimensional social networks
Silvia D'Angelo, Marco Alfò, Michael Fop
Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterized by multiple social r…
Model-based Clustering with Sparse Covariance Matrices
Michael Fop, Thomas Brendan Murphy, Luca Scrucca
Finite Gaussian mixture models are widely used for model-based clustering of continuous data. Nevertheless, since the number of model parameters scales quadratically with the numbe…
Variable Selection Methods for Model-based Clustering
Michael Fop, Thomas Brendan Murphy
Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more comm…
Variable Selection for Latent Class Analysis with Application to Low Back Pain Diagnosis
Michael Fop, Keith Smart, Thomas Brendan Murphy
The identification of most relevant clinical criteria related to low back pain disorders may aid the evaluation of the nature of pain suffered in a way that usefully informs patien…
Zero & -inflated overdispersed binomial models for sum-constrained Poisson count processes
James Sweeney, John Haslett, Dipankar Bandyopadhyay +2
A frequent challenge encountered with compositional ecological data is how to interpret and model data with a high proportion of zeros and 's. Such data frequently occur in ecol…