6 citations · 10 across the 8 of their papers we have counts for
12 papers
Semi-supervised Gaussian mixture modelling with a missing-data mechanism in R
Ziyang Lyu, Daniel Ahfock, Ryan Thompson +1
Semi-supervised learning is being extensively applied to estimate classifiers from training data in which not all the labels of the feature vectors are available. We present gmmssl…
Analysis of Estimating the Bayes Rule for Gaussian Mixture Models with a Specified Missing-Data Mechanism
Ziyang Lyu
Semi-supervised learning (SSL) approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framewor…
On randomized sketching algorithms and the Tracy-Widom law
Daniel Ahfock, William J. Astle, Sylvia Richardson
There is an increasing body of work exploring the integration of random projection into algorithms for numerical linear algebra. The primary motivation is to reduce the overall com…
Data-fusion using factor analysis and low-rank matrix completion
Daniel Ahfock, Saumyadipta Pyne, Geoffrey J. McLachlan
Data-fusion involves the integration of multiple related datasets. The statistical file-matching problem is a canonical data-fusion problem in multivariate analysis, where the obje…
Harmless label noise and informative soft-labels in supervised classification
Daniel Ahfock, Geoffrey J. McLachlan
Manual labelling of training examples is common practice in supervised learning. When the labelling task is of non-trivial difficulty, the supplied labels may not be equal to the g…
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
Daniel Ahfock, Geoffrey J. McLachlan
There has been increasing attention to semi-supervised learning (SSL) approaches in machine learning to forming a classifier in situations where the training data for a classifier…