Exploratory Data Analysis Using Radial Basis Function Latent Variable Models
Abstract
Two developments of nonlinear latent variable models based on radial basis functions are discussed: in the first, the use of priors or constraints on allowable models is considered as a means of preserving data structure in low-dimensional representations for visualisation purposes. Also, a resampling approach is introduced which makes more effective use of the latent samples in evaluating the likelihood.
Cite
Text
Marrs and Webb. "Exploratory Data Analysis Using Radial Basis Function Latent Variable Models." Neural Information Processing Systems, 1998.Markdown
[Marrs and Webb. "Exploratory Data Analysis Using Radial Basis Function Latent Variable Models." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/marrs1998neurips-exploratory/)BibTeX
@inproceedings{marrs1998neurips-exploratory,
title = {{Exploratory Data Analysis Using Radial Basis Function Latent Variable Models}},
author = {Marrs, Alan D. and Webb, Andrew R.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {529-535},
url = {https://mlanthology.org/neurips/1998/marrs1998neurips-exploratory/}
}