7 papers
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…