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

Distinguishing the importance of views has proven to be quite helpful forsemi-supervised multi-view learning models. However, existing strategies cannottake advantage of semi-supervised information, only distinguishing theimportance of views from a data feature perspective, which is often influencedby low-quality views then leading to poor performance. In this paper, byestablishing a link between labeled data and the importance of different views,we propose an auto-weighted strategy to evaluate the importance of views from alabel perspective to avoid the negative impact of unimportant or low-qualityviews. Based on this strategy, we propose a transductive semi-supervisedauto-weighted multi-view classification model. The initialization of theproposed model can be effectively determined by labeled data, which ispractical. The model is decoupled into three small-scale sub-problems that canefficiently be optimized with a local convergence guarantee. The experimentalresults on classification tasks show that the proposed method achieves optimalor sub-optimal classification accuracy at the lowest computational costcompared to other related methods, and the weight change experiments show thatour proposed strategy can distinguish view importance more accurately thanother related strategies on multi-view datasets with low-quality views.

Multi-view Data Classification with a Label-driven Auto-weighted Strategy