English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The interpretation of complex high-dimensional data typically requires theuse of dimensionality reduction techniques to extract explanatorylow-dimensional representations. However, in many real-world problems theserepresentations may not be sufficient to aid interpretation on their own, andit would be desirable to interpret the model in terms of the original featuresthemselves. Our goal is to characterise how feature-level variation depends onlatent low-dimensional representations, external covariates, and non-linearinteractions between the two. In this paper, we propose to achieve this througha structured kernel decomposition in a hybrid Gaussian Process model which wecall the Covariate Gaussian Process Latent Variable Model (c-GPLVM). Wedemonstrate the utility of our model on simulated examples and applications indisease progression modelling from high-dimensional gene expression data in thepresence of additional phenotypes. In each setting we show how the c-GPLVM canextract low-dimensional structures from high-dimensional data sets whilstallowing a breakdown of feature-level variability that is not present in othercommonly used dimensionality reduction approaches.

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models