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

The statistical shape analysis called Procrustes analysis minimizes thedistance between matrices by similarity transformations. The method returns aset of optimal orthogonal matrices, which project each matrix into a commonspace. This manuscript presents two types of distances derived from Procrustesanalysis for exploring between-matrices similarity. The first one focuses onthe residuals from the Procrustes analysis, i.e., the residual-based distancemetric. In contrast, the second one exploits the fitted orthogonal matrices,i.e., the rotational-based distance metric. Thanks to these distances,similarity-based techniques such as the multidimensional scaling method can beapplied to visualize and explore patterns and similarities among observations.The proposed distances result in being helpful in functional magnetic resonanceimaging (fMRI) data analysis. The brain activation measured over space and timecan be represented by a matrix. The proposed distances applied to a sample ofsubjects -- i.e., matrices -- revealed groups of individuals sharing patternsof neural brain activation.

Procrustes-based distances for exploring between-matrices similarity