From the 1 of 10 linked papers with an AI index.
10 papers
Data compression for fast dimension reduction and clustering of high-dimensional discrete data
Silvia D'Angelo, Michael Fop
The paper proposes a deterministic compression method that transforms high‑dimensional binary or count data into a low‑dimensional continuous space using weighted sums from a scale…
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…
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…
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…
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…