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

Generalized matrix approximation plays a fundamental role in many machinelearning problems, such as CUR decomposition, kernel approximation, and matrixlow rank approximation. Especially with today's applications involved in largerand larger dataset, more and more efficient generalized matrix approximationalgorithems become a crucially important research issue. In this paper, we findnew sketching techniques to reduce the size of the original data matrix todevelop new matrix approximation algorithms. Our results derive a much tighterbound for the approximation than previous works: we obtain a $(1+\epsilon)$approximation ratio with small sketched dimensions which implies a moreefficient generalized matrix approximation.

Tighter bound of Sketched Generalized Matrix Approximation