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 propose a modification of linear discriminant analysis, referred to ascompressive regularized discriminant analysis (CRDA), for analysis ofhigh-dimensional datasets. CRDA is specially designed for feature eliminationpurpose and can be used as gene selection method in microarray studies. CRDAlends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiplemeasurement vectors (MMV) model and utilizes joint-sparsity promoting hardthresholding for feature elimination. A regularization of the sample covariancematrix is also needed as we consider the challenging scenario where the numberof features (variables) is comparable or exceeding the sample size of thetraining dataset. A simulation study and four examples of real-life microarraydatasets evaluate the performances of CRDA based classifiers. Overall, theproposed method gives fewer misclassification errors than its competitors,while at the same time achieving accurate feature selection.

Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies