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

For a certain class of distributions, we prove that the linear programmingrelaxation of $k$-medoids clustering---a variant of $k$-means clustering wheremeans are replaced by exemplars from within the dataset---distinguishes pointsdrawn from nonoverlapping balls with high probability once the number of pointsdrawn and the separation distance between any two balls are sufficiently large.Our results hold in the nontrivial regime where the separation distance issmall enough that points drawn from different balls may be closer to each otherthan points drawn from the same ball; in this case, clustering by thresholdingpairwise distances between points can fail. We also exhibit numerical evidenceof high-probability recovery in a substantially more permissive regime.

Recovery guarantees for exemplar-based clustering