71 citations · 110 across the 48 of their papers we have counts for
15 papers · 1 filter
Mini-batch learning of exponential family finite mixture models
H D Nguyen, F Forbes, G J McLachlan
Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectatio…
Randomized Mixture Models for Probability Density Approximation and Estimation
Hien D. Nguyen, Dianhui Wang, Geoffrey J. McLachlan
Randomized neural networks (NNs) are an interesting alternative to conventional NNs that are more used for data modeling. The random vector functional-link (RVFL) network is an est…
Positive data kernel density estimation via the logKDE package for R
Andrew T. Jones, Hien D. Nguyen, Geoffrey J. McLachlan
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric estimation of probability density functions (PDFs), when data are obtained from unknown data generating proc…
A Novel Algorithm for Clustering of Data on the Unit Sphere via Mixture Models
Hien D. Nguyen
A new maximum approximate likelihood (ML) estimation algorithm for the mixture of Kent distribution is proposed. The new algorithm is constructed via the BSLM (block successive low…
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Hien D. Nguyen, Geoffrey J. McLachlan
Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpos…
A Stream-Suitable Kolmogorov-Smirnov-Type Test for Big Data Analysis
Hien Duy Nguyen
Big Data has become an ever more commonplace setting that is encountered by data analysts. In the Big Data setting, analysts are faced with very large numbers of observations as we…
A Simple Online Parameter Estimation Technique with Asymptotic Guarantees
Hien D Nguyen
In many modern settings, data are acquired iteratively over time, rather than all at once. Such settings are known as online, as opposed to offline or batch. We introduce a simple…
A Note on the Convergence of the Gaussian Mean Shift Algorithm
Hien D Nguyen
Mean shift (MS) algorithms are popular methods for mode finding in pattern analysis. Each MS algorithm can be phrased as a fixed-point iteration scheme, which operates on a kernel…
Chunked-and-Averaged Estimators for Vector Parameters
Hien D. Nguyen, Geoffrey J. McLachlan
A divide-and-conquer method for parameter estimation is the chunked-and-averaged (CA) estimator. CA estimators have been studied for univariate parameters under independent and ide…
An Introduction to MM Algorithms for Machine Learning and Statistical
Hien D. Nguyen
MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduce…
Faster Functional Clustering via Gaussian Mixture Models
Hien D Nguyen, Geoffrey J McLachlan, Jeremy F P Ullmann +1
Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is model-based clustering, which organizes functiona…
A Block Minorization--Maximization Algorithm for Heteroscedastic Regression
Hien D. Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan
The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multi…
Maximum Likelihood Estimation of Triangular and Polygonal Distributions
Hien D Nguyen, Geoffrey J McLachlan
Triangular distributions are a well-known class of distributions that are often used as elementary example of a probability model. In the past, enumeration and order statistic-base…
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data
Hien D Nguyen, Geoffrey J McLachlan, Pierre Orban +2
Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires the ev…
Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines
Hien D. Nguyen, Ian A. Wood
Boltzmann machines (BMs) are a class of binary neural networks for which there have been numerous proposed methods of estimation. Recently, it has been shown that in the fully visi…