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

We present a new approach for computing compact sketches that can be used toapproximate the inner product between pairs of high-dimensional vectors. Basedon the Weighted MinHash algorithm, our approach admits strong accuracyguarantees that improve on the guarantees of popular linear sketchingapproaches for inner product estimation, such as CountSketch andJohnson-Lindenstrauss projection. Specifically, while our method admitsguarantees that exactly match linear sketching for dense vectors, it yieldssignificantly lower error for sparse vectors with limited overlap betweennon-zero entries. Such vectors arise in many applications involving sparsedata. They are also important in increasingly popular dataset searchapplications, where inner product sketches are used to estimate datacovariance, conditional means, and other quantities involving columns inunjoined tables. We complement our theoretical results by showing that ourapproach empirically outperforms existing linear sketches and unweightedhashing-based sketches for sparse vectors.

Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation