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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper presents a novel pairwise constraint propagation approach bydecomposing the challenging constraint propagation problem into a set ofindependent semi-supervised learning subproblems which can be solved inquadratic time using label propagation based on k-nearest neighbor graphs.Considering that this time cost is proportional to the number of all possiblepairwise constraints, our approach actually provides an efficient solution forexhaustively propagating pairwise constraints throughout the entire dataset.The resulting exhaustive set of propagated pairwise constraints are furtherused to adjust the similarity matrix for constrained spectral clustering. Otherthan the traditional constraint propagation on single-source data, our approachis also extended to more challenging constraint propagation on multi-sourcedata where each pairwise constraint is defined over a pair of data points fromdifferent sources. This multi-source constraint propagation has an importantapplication to cross-modal multimedia retrieval. Extensive results have shownthe superior performance of our approach.

Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications