Search Results for author: Paul D. McNicholas

Found 25 papers, 4 papers with code

Clustering Higher Order Data: An Application to Pediatric Multi-variable Longitudinal Data

no code implementations19 Jul 2019 Peter A. Tait, Paul D. McNicholas, Joyce Obeid

Physical activity levels are an important predictor of cardiovascular health and increasingly being measured by sensors, like accelerometers.


Finding Outliers in Gaussian Model-Based Clustering

no code implementations2 Jul 2019 Katharine M. Clark, Paul D. McNicholas

Unsupervised classification, or clustering, is a problem often plagued by outliers, yet there is a paucity of work on handling outliers in unsupervised classification.


Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

no code implementations12 Mar 2019 Michael P. B. Gallaugher, Yang Tang, Paul D. McNicholas

A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed by including a penalty term in the likelihood constraining the parameters resulting in a flexible model for high dimensional data and a meaningful interpretation.


Clustering Discrete-Valued Time Series

no code implementations26 Jan 2019 Tyler Roick, Dimitris Karlis, Paul D. McNicholas

The INAR type models can be used in conjunction with existing model-based clustering techniques to cluster discrete-valued time series data.

Clustering Model Selection +2

Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes

no code implementations23 Dec 2018 Forrest Paton, Paul D. McNicholas

Functional data analysis is a statistical framework where data are assumed to follow some functional form.

Clustering Gaussian Processes +2

An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

no code implementations31 Oct 2018 Sharon M. McNicholas, Paul D. McNicholas, Daniel A. Ashlock

An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering.

Clustering General Classification

Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers

1 code implementation7 Sep 2018 Michael P. B. Gallaugher, Paul D. McNicholas

In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering.

Clustering Dimensionality Reduction

Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data

1 code implementation22 Jul 2018 Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi

Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data.


A Latent Gaussian Mixture Model for Clustering Longitudinal Data

no code implementations13 Apr 2018 Vanessa S. E. Bierling, Paul D. McNicholas

Amongst other uses, they have been applied for clustering longitudinal data and clustering high-dimensional data.

Clustering Model Selection

Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models

no code implementations13 Feb 2018 Michael P. B. Gallaugher, Paul D. McNicholas

A mixture of first-order continuous time Markov models is introduced for unsupervised and semi-supervised learning of clickstream data.

Clustering General Classification

A Mixture of Matrix Variate Bilinear Factor Analyzers

1 code implementation22 Dec 2017 Michael P. B. Gallaugher, Paul D. McNicholas

This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification.

Clustering Dimensionality Reduction +1

A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data

2 code implementations30 Nov 2017 Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi

The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products.

Methodology Quantitative Methods Computation

Model Based Clustering of High-Dimensional Binary Data

no code implementations11 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.

Clustering Vocal Bursts Intensity Prediction

Asymmetric Clusters and Outliers: Mixtures of Multivariate Contaminated Shifted Asymmetric Laplace Distributions

no code implementations26 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).

Families of Parsimonious Finite Mixtures of Regression Models

no code implementations2 Dec 2013 Utkarsh J. Dang, Paul D. McNicholas

Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies.


A Mixture of Generalized Hyperbolic Factor Analyzers

no code implementations26 Nov 2013 Cristina Tortora, Paul D. McNicholas, Ryan P. Browne

Model-based clustering imposes a finite mixture modelling structure on data for clustering.


Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions

no code implementations7 Sep 2013 Sanjeena Subedi, Paul D. McNicholas

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations.


Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures

no code implementations28 Aug 2013 Katherine Morris, Paul D. McNicholas

This mixture model-based approach is based on fitting generalized hyperbolic mixtures on a reduced subspace within the paradigm of model-based clustering, classification, or discriminant analysis.

Clustering Dimensionality Reduction +1

Standardizing Interestingness Measures for Association Rules

no code implementations16 Aug 2013 Mateen Shaikh, Paul D. McNicholas, M. Luiza Antonie, T. Brendan Murphy

However, properties of individual association rules restrict the values an interestingness measure can achieve.

Mixtures of Common Skew-t Factor Analyzers

no code implementations21 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.


Fractionally-Supervised Classification

no code implementations13 Jul 2013 Irene Vrbik, Paul D. McNicholas

When some observations are unlabelled, it can be very difficult to \textit{a~priori} choose the optimal level of supervision, and the consequences of a sub-optimal choice can be non-trivial.

Classification Clustering +1

A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting

no code implementations23 Jun 2013 Sanjeena Subedi, Paul D. McNicholas

Within the family setting, model selection involves choosing the member of the family, i. e., the appropriate covariance structure, in addition to the number of mixture components.

Clustering Model Selection

Mixture Model Averaging for Clustering

no code implementations23 Dec 2012 Yuhong Wei, Paul D. McNicholas

In mixture model-based clustering applications, it is common to fit several models from a family and report clustering results from only the `best' one.

Clustering Model Selection

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