2 code implementations • 15 Feb 2021 • Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne, Matthias Schonlau
We propose a novel, modular method for generating soft-label prototypical lines that still maintains representational accuracy even when there are fewer prototypes than the number of classes in the data.
no code implementations • 3 Nov 2014 • Utkarsh J. Dang, Antonio Punzo, Paul D. McNicholas, Salvatore Ingrassia, Ryan P. Browne
A family of parsimonious Gaussian cluster-weighted models is presented.
no code implementations • 11 Apr 2014 • Yang Tang, Ryan P. Browne, Paul D. McNicholas
Recent work on clustering of binary data, based on a $d$-dimensional Gaussian latent variable, is extended by incorporating common factor analyzers.
no code implementations • 26 Feb 2014 • Katherine Morris, Antonio Punzo, Paul D. McNicholas, Ryan P. Browne
Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein).
no code implementations • 26 Nov 2013 • Cristina Tortora, Paul D. McNicholas, Ryan P. Browne
Model-based clustering imposes a finite mixture modelling structure on data for clustering.
no code implementations • 1 Nov 2013 • Brian C. Franczak, Paul D. McNicholas, Ryan P. Browne, Paula M. Murray
A family of parsimonious shifted asymmetric Laplace mixture models is introduced.
no code implementations • 21 Jul 2013 • Paula M. Murray, Paul D. McNicholas, Ryan P. Browne
A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data.
no code implementations • 25 Jun 2013 • Ryan P. Browne, Sanjeena Subedi, Paul McNicholas
Previous work has focused on circumventing this problem by constraining the smallest eigenvalue of the component covariance matrices.