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 introduce a novel co-learning paradigm for manifolds naturally equippedwith a group action, motivated by recent developments on learning a manifoldfrom attached fibre bundle structures. We utilize a representation theoreticmechanism that canonically associates multiple independent vector bundles overa common base manifold, which provides multiple views for the geometry of theunderlying manifold. The consistency across these fibre bundles provide acommon base for performing unsupervised manifold co-learning through theredundancy created artificially across irreducible representations of thetransformation group. We demonstrate the efficacy of the proposed algorithmicparadigm through drastically improved robust nearest neighbor search andcommunity detection on rotation-invariant cryo-electron microscopy imageanalysis.

Unsupervised Co-Learning on $\mathcal{G}$-Manifolds Across Irreducible Representations