12 citations · 28 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
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…