Multi-level Visualisation using Gaussian Process Latent Variable Models

Shahzad Mumtaz, Darren R. Flower, Ian T. Nabney

2014

Abstract

Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the ‘Major Histocompatibility Complex (MHC) class I’ of humans. In both cases, visual observation and the quantitative quality measures have shown better visualisation at lower levels.

Paper Citation

in Harvard Style

Mumtaz S., Flower D. and Nabney I. (2014). Multi-level Visualisation using Gaussian Process Latent Variable Models . In Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014) ISBN 978-989-758-005-5, pages 122-129. DOI: 10.5220/0004686801220129

in Bibtex Style

@conference{ivapp14,
author={Shahzad Mumtaz and Darren R. Flower and Ian T. Nabney},
title={Multi-level Visualisation using Gaussian Process Latent Variable Models},
booktitle={Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)},
year={2014},
pages={122-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004686801220129},
isbn={978-989-758-005-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)
TI - Multi-level Visualisation using Gaussian Process Latent Variable Models
SN - 978-989-758-005-5
AU - Mumtaz S.
AU - Flower D.
AU - Nabney I.
PY - 2014
SP - 122
EP - 129
DO - 10.5220/0004686801220129