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

Low-Rank Representation (LRR) suffers from discarding the locality information of data points in subspace clustering. We propose a hypergraph-based model by incorporation of a variable number of adjacent nodes, locality information, and sparsity of subspaces. An optimization problem is defined and solved by developing a tensor Laplacian-based algorithm.The outperformance of the proposed method is substantial when the inherent structure of the data exploits severe nonlinearity, geometrical overlapping, and outliers.

Tensor Laplacian Regularized Low-Rank Representation for Non-Uniformly Distributed Data Subspace Clustering