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/

Zendy Plus

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

Zendy Tools

Zendy Open

In the field of unsupervised feature selection, sparse principal componentanalysis (SPCA) methods have attracted more and more attention recently.Compared to spectral-based methods, SPCA methods don't rely on the constructionof a similarity matrix and show better feature selection ability on real-worlddata. The original SPCA formulates a nonconvex optimization problem. Existingconvex SPCA methods reformulate SPCA as a convex model by regarding thereconstruction matrix as an optimization variable. However, they are lack ofconstraints equivalent to the orthogonality restriction in SPCA, leading tolarger solution space. In this paper, it's proved that the optimal solution toa convex SPCA model falls onto the Positive Semidefinite (PSD) cone. A standardconvex SPCA-based model with PSD constraint for unsupervised feature selectionis proposed. Further, a two-step fast optimization algorithm via PSD projectionis presented to solve the proposed model. Two other existing convex SPCA-basedmodels are also proven to have their solutions optimized on the PSD cone inthis paper. Therefore, the PSD versions of these two models are proposed toaccelerate their convergence as well. We also provide a regularizationparameter setting strategy for our proposed method. Experiments on syntheticand real-world datasets demonstrate the effectiveness and efficiency of theproposed methods.

Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection