7 citations · 16 across the 12 of their papers we have counts for
13 papers
Efficient Estimation of High Information Projections using Nearest Neighbours
David P. Hofmeyr
An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interesting projections of multivariate data. Following similar intuitive motiva…
Clustering by Nonparametric Smoothing
David P. Hofmeyr
A novel formulation of the clustering problem is introduced in which the task is expressed as an estimation problem, where the object to be estimated is a function which maps a poi…
Optimal Projections for Classification with Naive Bayes
David P. Hofmeyr, Francois Kamper, Michail C. Melonas
In the Naive Bayes classification model the class conditional densities are estimated as the products of their marginal densities along the cardinal basis directions. We study the…
Getting more from your regression model: A free lunch?
David P. Hofmeyr
We consider a simple approach for approximating detailed information about the conditional distribution of a real-valued response variable, given values for its covariates, using o…
Clustering Large Data Sets with Incremental Estimation of Low-density Separating Hyperplanes
David P. Hofmeyr
An efficient method for obtaining low-density hyperplane separators in the unsupervised context is proposed. Low density separators can be used to obtain a partition of a set of da…
Optimal Projections for Gaussian Discriminants
David P. Hofmeyr, Francois Kamper, Michail C. Melonas
The problem of obtaining optimal projections for performing discriminant analysis with Gaussian class densities is studied. Unlike in most existing approaches to the problem, the f…
Fast Kernel Smoothing in R with Applications to Projection Pursuit
David P. Hofmeyr
This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapp…
Connecting Spectral Clustering to Maximum Margins and Level Sets
David P. Hofmeyr
We study the connections between spectral clustering and the problems of maximum margin clustering, and estimation of the components of level sets of a density function. Specifical…
Degrees of Freedom and Model Selection for k-means Clustering
David P. Hofmeyr
This paper investigates the model degrees of freedom in k-means clustering. An extension of Stein's lemma provides an expression for the effective degrees of freedom in the k-means…
Fast Exact Univariate Kernel Density Estimation
David P. Hofmeyr
This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density es…
Improving Spectral Clustering using the Asymptotic Value of the Normalised Cut
David Hofmeyr
Spectral clustering is a popular and versatile clustering method based on a relaxation of the normalised graph cut objective. Despite its popularity, however, there is no single ag…
Minimum Spectral Connectivity Projection Pursuit
David P. Hofmeyr, Nicos G. Pavlidis, Idris A. Eckley
We study the problem of determining the optimal low dimensional projection for maximising the separability of a binary partition of an unlabelled dataset, as measured by spectral g…
Minimum Density Hyperplanes
Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning a…