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

In this paper, we address the problem of learning compactsimilarity-preserving embeddings for massive high-dimensional streams of datain order to perform efficient similarity search. We present a new online methodfor computing binary compressed representations -sketches- of high-dimensionalreal feature vectors. Given an expected code length $c$ and high-dimensionalinput data points, our algorithm provides a $c$-bits binary code for preservingthe distance between the points from the original high-dimensional space. Ouralgorithm does not require neither the storage of the whole dataset nor achunk, thus it is fully adaptable to the streaming setting. It also provideslow time complexity and convergence guarantees. We demonstrate the quality ofour binary sketches through experiments on real data for the nearest neighborssearch task in the online setting.

Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization