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

Because of the attractiveness of the canonical polyadic (CP) tensordecomposition in various applications, several algorithms have been designed tocompute it, but efficient ones are still lacking. Iterative deflationalgorithms based on successive rank-1 approximations can be used to performthis task, since the latter are rather easy to compute. We first present analgebraic rank-1 approximation method that performs better than the standardhigher-order singular value decomposition (HOSVD) for three-way tensors.Second, we propose a new iterative rank-1 approximation algorithm that improvesany other rank-1 approximation method. Third, we describe a probabilisticframework allowing to study the convergence of deflation CP decomposition(DCPD) algorithms based on successive rank-1 approximations. A set of computerexperiments then validates theoretical results and demonstrates the efficiencyof DCPD algorithms compared to other ones.

Rank-1 Tensor Approximation Methods and Application to Deflation