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

We present an approach for performing linear discriminant a nalysis (LDA) in the contemporary challenging context of high dimensionality. The projection matrix o f LDA is usually obtained by simultaneously maximizing the between-class covariance and minimizing th e within-class covariance. However it involves matrix eigendecomposition which is computationally expen sive in both time and memory requirement when the number of samples and the number of features are large. To deal with this complexity, we propose to use a recent dimension reduction method. The technique is based o n fast approximate singular value decomposition (SVD) which has deep connections with low-rank approximati on of the data matrix. The proposed approach, appSVD+LDA, consists of two stages. The first stage leads to a set of artificial features based on the original data. The second stage is the classical LDA. The foundation o f ur approach is presented and its performances in term of accuracy and computation time in comparison with s ome state-of-the-art techniques are provided for different real data sets.

Linear Discriminant Analysis based on Fast Approximate SVD