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

In exploratory tensor mining, a common problem is how to analyze a set ofvariables across a set of subjects whose observations do not align naturally.For example, when modeling medical features across a set of patients, thenumber and duration of treatments may vary widely in time, meaning there is nomeaningful way to align their clinical records across time points for analysispurposes. To handle such data, the state-of-the-art tensor model is theso-called PARAFAC2, which yields interpretable and robust output and cannaturally handle sparse data. However, its main limitation up to now has beenthe lack of efficient algorithms that can handle large-scale datasets.  In this work, we fill this gap by developing a scalable method to compute thePARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our methodexploits special structure within PARAFAC2, leading to a novel algorithmicreformulation that is both fast (in absolute time) and more memory-efficientthan prior work. We evaluate SPARTan on both synthetic and real datasets,showing 22X performance gains over the best previous implementation and alsohandling larger problem instances for which the baseline fails. Furthermore, weare able to apply SPARTan to the mining of temporally-evolving phenotypes ondata taken from real and medically complex pediatric patients. The clinicalmeaningfulness of the phenotypes identified in this process, as well as theirtemporal evolution over time for several patients, have been endorsed byclinical experts.

SPARTan: Scalable PARAFAC2 for Large & Sparse Data