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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a novel classification model for weak signal data, building upon arecent model for Bayesian multi-view learning, Group Factor Analysis (GFA).Instead of assuming all data to come from a single GFA model, we allow latentclusters, each having a different GFA model and producing a different classdistribution. We show that sharing information across the clusters, by sharingfactors, increases the classification accuracy considerably; the shared factorsessentially form a flexible noise model that explains away the part of data notrelated to classification. Motivation for the setting comes from single-trialfunctional brain imaging data, having a very low signal-to-noise ratio and anatural multi-view setting, with the different sensors, measurement modalities(EEG, MEG, fMRI) and possible auxiliary information as views. We demonstrateour model on a MEG dataset.

Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers