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

Tucker decomposition is a powerful tensor model to handle multi-aspect data.It demonstrates the low-rank property by decomposing the grid-structured dataas interactions between a core tensor and a set of object representations(factors). A fundamental assumption of such decomposition is that there werefinite objects in each aspect or mode, corresponding to discrete indexes ofdata entries. However, many real-world data are not naturally posed in thesetting. For example, geographic data is represented as continuous indexes oflatitude and longitude coordinates, and cannot fit tensor models directly. Togeneralize Tucker decomposition to such scenarios, we propose FunctionalBayesian Tucker Decomposition (FunBaT). We treat the continuous-indexed data asthe interaction between the Tucker core and a group of latent functions. We useGaussian processes (GP) as functional priors to model the latent functions, andthen convert the GPs into a state-space prior by constructing an equivalentstochastic differential equation (SDE) to reduce computational cost. Anefficient inference algorithm is further developed for scalable posteriorapproximation based on advanced message-passing techniques. The advantage ofour method is shown in both synthetic data and several real-world applications.

Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data