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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study the underlying structure of data (approximately) generated from aunion of independent subspaces. Traditional methods learn only one subspace,failing to discover the multi-subspace structure, while state-of-the-artmethods analyze the multi-subspace structure using data themselves as thedictionary, which cannot offer the explicit basis to span each subspace and aresensitive to errors via an indirect representation. Additionally, they alsosuffer from a high computational complexity, being quadratic or cubic to thesample size. To tackle all these problems, we propose a method, called MatrixFactorization with Column L0-norm constraint (MFC0), that can simultaneouslylearn the basis for each subspace, generate a direct sparse representation foreach data sample, as well as removing errors in the data in an efficient way.Furthermore, we develop a first-order alternating direction algorithm, whosecomputational complexity is linear to the sample size, to stably andeffectively solve the nonconvex objective function and non- smooth l0-normconstraint of MFC0. Experimental results on both synthetic and real-worlddatasets demonstrate that besides the superiority over traditional andstate-of-the-art methods for subspace clustering, data reconstruction, errorcorrection, MFC0 also shows its uniqueness for multi-subspace basis learningand direct sparse representation.

Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization