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

High-dimensional complex systems can be studied through multivariateanalysis, as Principal Component Analysis, however large samples ofobservations frequently are needed for it. Here it is examined a method forsmall samples based on clustering variables around latent variables (CLV) tosubject classification in two presumed groups. For it, a predictive model wasdeveloped to generate datasets with two groups of cases whose variables showrandomness features (up to 30% of variables manifest difference between groups,and up to 7% of those are correlated between them). The method recovered theinformation of the latent factors to classify the subjects with 80 to 95% ofagreement, with positive relationship between the classifier precision and therate [number of variables / number of subjects].

Subjects classification from high-dimensional and small-sample size  datasets using a strategy based on Clustering Variables around Latent  Components (CLV) method