Multi-level visualisation using Gaussian process latent variable models

Shahzad Mumtaz, Darren R. Flower, Ian Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationIVAPP 2014
EditorsRobert S. Laramee, Andreas Kerren, José Braz
Number of pages8
ISBN (Print)978-989-758-005-5
Publication statusPublished - 2014


  • continuity, Gaussian mixture model, K-means, major histocompatibility complex, mean relative rank errors, multi-level Gaussian process latent variable model, negative log-likelihood, trustworthiness, visualisation distance distortion


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