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/

Zendy Plus

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

Zendy Tools

Zendy Open

Low rank tensor completion is a well studied problem and has applications invarious fields. However, in many real world applications the data is dynamic,i.e., new data arrives at different time intervals. As a result, the tensorsused to represent the data grow in size. Besides the tensors, in many realworld scenarios, side information is also available in the form of matriceswhich also grow in size with time. The problem of predicting missing values inthe dynamically growing tensor is called dynamic tensor completion. Most of theprevious work in dynamic tensor completion make an assumption that the tensorgrows only in one mode. To the best of our Knowledge, there is no previous workwhich incorporates side information with dynamic tensor completion. We bridgethis gap in this paper by proposing a dynamic tensor completion frameworkcalled Side Information infused Incremental Tensor Analysis (SIITA), whichincorporates side information and works for general incremental tensors. Wealso show how non-negative constraints can be incorporated with SIITA, which isessential for mining interpretable latent clusters. We carry out extensiveexperiments on multiple real world datasets to demonstrate the effectiveness ofSIITA in various different settings.

Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information