Geoffrey J. McLachlan

According to our database, Geoffrey J. McLachlan authored at least 113 papers between 1976 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

A new algorithm for support vector regression with automatic selection of hyperparameters.

You-Gan Wang, Jinran Wu, Zhi-Hua Hu, Geoffrey J. McLachlan

Pattern Recognit., 2023

2022

An overview of skew distributions in model-based clustering.

Sharon X. Lee, Geoffrey J. McLachlan

J. Multivar. Anal., 2022

Statistical file-matching of non-Gaussian data: A game theoretic approach.

Daniel Ahfock, Saumyadipta Pyne, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2022

Some Simulation and Empirical Results for Semi-Supervised Learning of the Bayes Rule of Allocation.

Ziyang Lyu, Daniel Ahfock, Geoffrey J. McLachlan

CoRR, 2022

Functional Mixtures-of-Experts.

Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang, Geoffrey J. McLachlan

CoRR, 2022

2021

Multi-node Expectation-Maximization algorithm for finite mixture models.

Sharon X. Lee, Geoffrey J. McLachlan, Kaleb L. Leemaqz

Stat. Anal. Data Min., 2021

Data fusion using factor analysis and low-rank matrix completion.

Daniel Ahfock, Saumyadipta Pyne, Geoffrey J. McLachlan

Stat. Comput., 2021

Skew-normal generalized spatial panel data model.

Mohadeseh Alsadat Farzammehr, Mohammad Reza Zadkarami, Geoffrey J. McLachlan

Commun. Stat. Simul. Comput., 2021

Harmless label noise and informative soft-labels in supervised classification.

Daniel Ahfock, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2021

Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review.

Daniel Ahfock, Geoffrey J. McLachlan

CoRR, 2021

Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions.

Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan

Adv. Data Anal. Classif., 2021

2020

Mini-batch learning of exponential family finite mixture models.

Hien Duy Nguyen, Florence Forbes, Geoffrey J. McLachlan

Stat. Comput., 2020

An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified.

Daniel Ahfock, Geoffrey J. McLachlan

Stat. Comput., 2020

An l₁-oracle inequality for the Lasso in mixture-of-experts regression models.

TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi, Geoffrey J. McLachlan

CoRR, 2020

Estimation of Classification Rules from Partially Classified Data.

Geoffrey J. McLachlan, Daniel Ahfock

CoRR, 2020

2019

Deep Gaussian mixture models.

Cinzia Viroli, Geoffrey J. McLachlan

Stat. Comput., 2019

Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework.

Shu-Kay Ng, Richard Tawiah, Geoffrey J. McLachlan

Pattern Recognit., 2019

PPEM: Privacy-preserving EM learning for mixture models.

Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan

Concurr. Comput. Pract. Exp., 2019

2018

A Block EM Algorithm for Multivariate Skew Normal and Skew $t$ -Mixture Models.

Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan

IEEE Trans. Neural Networks Learn. Syst., 2018

Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling.

Hien Duy Nguyen, Jeremy F. P. Ullmann, Geoffrey J. McLachlan, Venkatakaushik Voleti, Wenze Li, Elizabeth M. C. Hillman, David C. Reutens, Andrew L. Janke

Stat. Anal. Data Min., 2018

logKDE: log-transformed kernel density estimation.

Andrew T. Jones, Hien Duy Nguyen, Geoffrey J. McLachlan

J. Open Source Softw., 2018

Randomized mixture models for probability density approximation and estimation.

Hien Duy Nguyen, Dianhui Wang, Geoffrey J. McLachlan

Inf. Sci., 2018

A globally convergent algorithm for lasso-penalized mixture of linear regression models.

Luke R. Lloyd-Jones, Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2018

Positive Data Kernel Density Estimation via the LogKDE Package for R.

Hien D. Nguyen, Andrew T. Jones, Geoffrey J. McLachlan

Proceedings of the Data Mining - 16th Australasian Conference, AusDM 2018, Bahrurst, NSW, 2018

2017

Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data.

Hien Duy Nguyen, Geoffrey J. McLachlan, Pierre Orban, Pierre Bellec, Andrew L. Janke

Neural Comput., 2017

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach.

Hien D. Nguyen, Geoffrey J. McLachlan

CoRR, 2017

Corruption-Resistant Privacy Preserving Distributed EM Algorithm for Model-Based Clustering.

Kaleb L. Leemaqz, Sharon X. Lee, Geoffrey J. McLachlan

Proceedings of the 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, Australia, August 1-4, 2017, 2017

Private Distributed Three-Party Learning of Gaussian Mixture Models.

Kaleb L. Leemaqz, Sharon X. Lee, Geoffrey J. McLachlan

Proceedings of the Applications and Techniques in Information Security, 2017

2016

A Block Minorization-Maximization Algorithm for Heteroscedastic Regression.

Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan

IEEE Signal Process. Lett., 2016

Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models.

Sharon X. Lee, Geoffrey J. McLachlan

Stat. Comput., 2016

A Universal Approximation Theorem for Mixture-of-Experts Models.

Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan

Neural Comput., 2016

Extending mixtures of factor models using the restricted multivariate skew-normal distribution.

Tsung-I Lin, Geoffrey J. McLachlan, Sharon X. Lee

J. Multivar. Anal., 2016

Mixtures of spatial spline regressions for clustering and classification.

Hien Duy Nguyen, Geoffrey J. McLachlan, Ian A. Wood

Comput. Stat. Data Anal., 2016

Linear mixed models with marginally symmetric nonparametric random effects.

Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

Maximum likelihood estimation of triangular and polygonal distributions.

Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

Laplace mixture of linear experts.

Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

Partial identification in the statistical matching problem.

Daniel Ahfock, Saumyadipta Pyne, Sharon X. Lee, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models.

Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan

Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications, 2016

Finding group structures in "Big Data" in healthcare research using mixture models.

Shu-Kay Ng, Geoffrey J. McLachlan

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2016

Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions.

Sharon X. Lee, Geoffrey J. McLachlan

Proceedings of the Advanced Data Mining and Applications - 12th International Conference, 2016

2015

Maximum likelihood estimation of Gaussian mixture models without matrix operations.

Hien Duy Nguyen, Geoffrey J. McLachlan

Adv. Data Anal. Classif., 2015

2014

On the number of components in a Gaussian mixture model.

Geoffrey J. McLachlan, Suren I. Rathnayake

WIREs Data Mining Knowl. Discov., 2014

False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields.

Hien Duy Nguyen, Geoffrey J. McLachlan, Nicolas Cherbuin, Andrew L. Janke

IEEE Trans. Medical Imaging, 2014

Finite mixtures of multivariate skew t-distributions: some recent and new results.

Sharon X. Lee, Geoffrey J. McLachlan

Stat. Comput., 2014

Mixture models for clustering multilevel growth trajectories.

Shu-Kay Ng, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2014

The 2nd special issue on advances in mixture models.

Dankmar Böhning, Christian Hennig, Geoffrey J. McLachlan, Paul D. McNicholas

Comput. Stat. Data Anal., 2014

Asymptotic inference for hidden process regression models.

Hien Duy Nguyen, Geoffrey J. McLachlan

Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014

2013

Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions".

Sharon X. Lee, Geoffrey J. McLachlan

Stat. Methods Appl., 2013

Model-based clustering and classification with non-normal mixture distributions.

Sharon X. Lee, Geoffrey J. McLachlan

Stat. Methods Appl., 2013

On the classification of microarray gene-expression data.

Kaye E. Basford, Geoffrey J. McLachlan, Suren I. Rathnayake

Briefings Bioinform., 2013

On mixtures of skew normal and skew t-distributions.

Sharon X. Lee, Geoffrey J. McLachlan

Adv. Data Anal. Classif., 2013

Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies.

Hien Duy Nguyen, Andrew L. Janke, Nicolas Cherbuin, Geoffrey J. McLachlan, Perminder S. Sachdev, Kaarin Anstey

Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications, 2013

A common factor-analytic model for classification.

Mingzhu Sun, Geoffrey J. McLachlan

Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013

Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes.

Shu-Kay Ng, Geoffrey J. McLachlan

Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013

2012

Top-10 Data Mining Case Studies.

Gabor Melli, Xindong Wu, Paul Beinat, Francesco Bonchi, Longbing Cao, Rong Duan, Christos Faloutsos, Rayid Ghani, Brendan Kitts, Bart Goethals, Geoffrey J. McLachlan, Jian Pei, Ashok Srivastava, Osmar R. Zaïane

Int. J. Inf. Technol. Decis. Mak., 2012

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects.

Kui Wang, Shu-Kay Ng, Geoffrey J. McLachlan

BMC Bioinform., 2012

An Enduring Interest in Classification: Supervised and Unsupervised.

Geoffrey J. McLachlan

Proceedings of the Journeys to Data Mining, 2012

2011

Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron.

Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan

Int. J. Comput. Intell. Appl., 2011

Mixtures of common <i>t</i>-factor analyzers for clustering high-dimensional microarray data.

Jangsun Baek, Geoffrey J. McLachlan

Bioinform., 2011

2010

Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data.

Jangsun Baek, Geoffrey J. McLachlan, Lloyd K. Flack

IEEE Trans. Pattern Anal. Mach. Intell., 2010

A Very Fast Algorithm for Matrix Factorization

Vladimir Nikulin, Tian-Hsiang Huang, Shu-Kay Ng, Suren I. Rathnayake, Geoffrey J. McLachlan

CoRR, 2010

Integrative mixture of experts to combine clinical factors and gene markers.

Kim-Anh Lê Cao, Emmanuelle Meugnier, Geoffrey J. McLachlan

Bioinform., 2010

Automated High-Dimensional Flow Cytometric Data Analysis.

Saumyadipta Pyne, Xinli Hu, Kui Wang, Elizabeth Rossin, Tsung-I Lin, Lisa Maier, Clare Baecher-Allan, Geoffrey J. McLachlan, Pablo Tamayo, David Hafler, Philip L. De Jager, Jill P. Mesirov

Proceedings of the Research in Computational Molecular Biology, 2010

Identifying fiber bundles with regularised к-means clustering applied to the grid-based data.

Vladimir Nikulin, Geoffrey J. McLachlan

Proceedings of the International Joint Conference on Neural Networks, 2010

Assessing the Significance of Groups in High-Dimensional Data.

Geoffrey J. McLachlan

Proceedings of the ICDM 2010, 2010

On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction.

Vladimir Nikulin, Geoffrey J. McLachlan

Proceedings of the BIOINFORMATICS 2010, 2010

A comparative study of two matrix factorization methods applied to the classification of gene expression data.

Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan

Proceedings of the 2010 IEEE International Conference on Bioinformatics and Biomedicine, 2010

2009

Classification of Imbalanced Marketing Data with Balanced Random Sets.

Vladimir Nikulin, Geoffrey J. McLachlan

Proceedings of KDD-Cup 2009 competition, Paris, France, June 28, 2009, 2009

Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data.

Kui Wang, Shu-Kay Ng, Geoffrey J. McLachlan

Proceedings of the DICTA 2009, 2009

Penalized Principal Component Analysis of Microarray Data.

Vladimir Nikulin, Geoffrey J. McLachlan

Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2009

Ensemble Approach for the Classification of Imbalanced Data.

Vladimir Nikulin, Geoffrey J. McLachlan, Shu-Kay Ng

Proceedings of the AI 2009: Advances in Artificial Intelligence, 2009

2008

Top 10 algorithms in data mining.

Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus F. M. Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael S. Steinbach, David J. Hand, Dan Steinberg

Knowl. Inf. Syst., 2008

Wallace's Approach to Unsupervised Learning: The Snob Program.

Murray A. Jorgensen, Geoffrey J. McLachlan

Comput. J., 2008

Clustering of High-Dimensional Data via Finite Mixture Models.

Geoffrey J. McLachlan, Jangsun Baek

Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

2007

Two-component Poisson mixture regression modelling of count data with bivariate random effects.

Kui Wang, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan

Math. Comput. Model., 2007

Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution.

Geoffrey J. McLachlan, Richard W. Bean, Liat Ben-Tovim Jones

Comput. Stat. Data Anal., 2007

Multilevel survival modelling of recurrent urinary tract infections.

Kui Wang, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan

Comput. Methods Programs Biomed., 2007

Segmentation and intensity estimation of microarray images using a gamma-t mixture model.

Jangsun Baek, Young Sook Son, Geoffrey J. McLachlan

Bioinform., 2007

Extension of mixture-of-experts networks for binary classification of hierarchical data.

Shu-Kay Ng, Geoffrey J. McLachlan

Artif. Intell. Medicine, 2007

Merging Algorithm to Reduce Dimensionality in Application to Web-Mining.

Vladimir Nikulin, Geoffrey J. McLachlan

Proceedings of the AI 2007: Advances in Artificial Intelligence, 2007

2006

Mixture Models for Detecting Differentially Expressed Genes in Microarrays.

Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu

Int. J. Neural Syst., 2006

A Mixture model with random-effects components for clustering correlated gene-expression profiles.

Shu-Kay Ng, Geoffrey J. McLachlan, Kui Wang, Liat Ben-Tovim Jones, S.-W. Ng

Bioinform., 2006

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays.

Geoffrey J. McLachlan, Richard W. Bean, Liat Ben-Tovim Jones

Bioinform., 2006

An incremental EM-based learning approach for on-line prediction of hospital resource utilization.

Shu-Kay Ng, Geoffrey J. McLachlan, Andy H. Lee

Artif. Intell. Medicine, 2006

2005

Application of Mixture Models to Detect Differentially Expressed Genes.

Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu

Proceedings of the Intelligent Data Engineering and Automated Learning, 2005

Cluster Analysis of High-Dimensional Data: A Case Study.

Richard Bean, Geoffrey J. McLachlan

Proceedings of the Intelligent Data Engineering and Automated Learning, 2005

Normalized Gaussian Networks with Mixed Feature Data.

Shu-Kay Ng, Geoffrey J. McLachlan

Proceedings of the AI 2005: Advances in Artificial Intelligence, 2005

2004

Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification.

Shu-Kay Ng, Geoffrey J. McLachlan

IEEE Trans. Neural Networks, 2004

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images.

Shu-Kay Ng, Geoffrey J. McLachlan

Pattern Recognit., 2004

On the Simultaneous Use of Clinical and Microarray Expression Data in the Cluster Analysis of Tissue Samples.

Geoffrey J. McLachlan, Soong Chang, Jess Mar, Christophe Ambroise, Justin Xi Zhu

Proceedings of the Second Asia-Pacific Bioinformatics Conference (APBC 2004), 2004

2003

On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures.

Shu-Kay Ng, Geoffrey J. McLachlan

Stat. Comput., 2003

Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data.

J. C. Mar, Geoffrey J. McLachlan

Int. J. Softw. Eng. Knowl. Eng., 2003

Modelling high-dimensional data by mixtures of factor analyzers.

Geoffrey J. McLachlan, David Peel, Richard W. Bean

Comput. Stat. Data Anal., 2003

Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees.

Shu-Kay Ng, Geoffrey J. McLachlan

Proceedings of the Seventh International Conference on Digital Image Computing: Techniques and Applications, 2003

2002

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data.

Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren

Mach. Learn., 2002

A mixture model-based approach to the clustering of microarray expression data.

Geoffrey J. McLachlan, Richard W. Bean, David Peel

Bioinform., 2002

2000

Robust mixture modelling using the t distribution.

David Peel, Geoffrey J. McLachlan

Stat. Comput., 2000

Mixtures of Factor Analyzers.

Geoffrey J. McLachlan, David Peel

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

Finite Mixture Models

Geoffrey J. McLachlan, David Peel

Wiley Series in Probability and Statistics, Wiley, ISBN: 978-0-47172118-5, 2000

1999

Hierarchical Models for Screening of Iron Deficiency Anemia.

Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan

Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27, 1999

1998

Robust Cluster Analysis via Mixtures of Multivariate t-Distributions.

Geoffrey J. McLachlan, David Peel

Proceedings of the Advances in Pattern Recognition, 1998

Mining in the Presence of Selectivity Bias and its Application to Reject Inference.

A. J. Feelders, Soong Chang, Geoffrey J. McLachlan

Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), 1998

MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models.

Geoffrey J. McLachlan, David Peel

Proceedings of the Fourteenth International Conference on Pattern Recognition, 1998

1996

Maximum likelihood clustering via normal mixture models.

Geoffrey J. McLachlan, David Peel, W. J. Whiten

Signal Process. Image Commun., 1996

1989

Bias associated with the discriminant analysis approach to the estimation of mixing proportions.

Charles R. O. Lawoko, Geoffrey J. McLachlan

Pattern Recognit., 1989

1988

Further results on discrimination with autocorrelated observations.

Charles R. O. Lawoko, Geoffrey J. McLachlan

Pattern Recognit., 1988

1986

Asymptotic error rates of the W and Z statistics when the training observations are dependent.

Charles R. O. Lawoko, Geoffrey J. McLachlan

Pattern Recognit., 1986

1985

Discrimination with autocorrelated observations.

Charles R. O. Lawoko, Geoffrey J. McLachlan

Pattern Recognit., 1985

1983

Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function.

Charles R. O. Lawoko, Geoffrey J. McLachlan

Pattern Recognit., 1983

1982

9 The classification and mixture maximum likelihood approaches to cluster analysis.

Geoffrey J. McLachlan

Proceedings of the Classification, Pattern Recognition and Reduction of Dimensionality, 1982

1980

Error rate estimation on the basis of posterior probabilities.

S. Ganesalingam, Geoffrey J. McLachlan

Pattern Recognit., 1980

1977

A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification.

Geoffrey J. McLachlan

Pattern Recognit., 1977

1976

Further results on the effect of intraclass correlation among training samples in discriminant analysis.

Geoffrey J. McLachlan

Pattern Recognit., 1976