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

In this paper we describe a procedure to reduce the size of the input featurevector. A complex pattern recognition problem like face recognition involveshuge dimension of input feature vector. To reduce that dimension here we haveused eigenspace projection (also called as Principal Component Analysis), whichis basically transformation of space. To reduce further we have applied featureselection method to select indispensable features, which will remain in thefinal feature vectors. Features those are not selected are removed from thefinal feature vector considering them as redundant or superfluous. Forselection of features we have used the concept of reduct and core from roughset theory. This method has shown very good performance. It is worth to mentionthat in some cases the recognition rate increases with the decrease in thefeature vector dimension.

Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition