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

Though manifold-based clustering has become a popular research topic, weobserve that one important factor has been omitted by these works, namely thatthe defined clustering loss may corrupt the local and global structure of thelatent space. In this paper, we propose a novel Generalized Clustering andMulti-manifold Learning (GCML) framework with geometric structure preservationfor generalized data, i.e., not limited to 2-D image data and has a wide rangeof applications in speech, text, and biology domains. In the proposedframework, manifold clustering is done in the latent space guided by aclustering loss. To overcome the problem that the clustering-oriented loss maydeteriorate the geometric structure of the latent space, an isometric loss isproposed for preserving intra-manifold structure locally and a ranking loss forinter-manifold structure globally. Extensive experimental results have shownthat GCML exhibits superior performance to counterparts in terms of qualitativevisualizations and quantitative metrics, which demonstrates the effectivenessof preserving geometric structure.

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation