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

We propose a second-order accurate method to estimate the eigenvectors ofextremely large matrices thereby addressing a problem of relevance tostatisticians working in the analysis of very large datasets. Morespecifically, we show that averaging eigenvectors of randomly subsampledmatrices efficiently approximates the true eigenvectors of the original matrixunder certain conditions on the incoherence of the spectral decomposition. Thisincoherence assumption is typically milder than those made in matrix completionand allows eigenvectors to be sparse. We discuss applications to spectralmethods in dimensionality reduction and information retrieval.

Second order accurate distributed eigenvector computation for extremely large matrices