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

Face recognition and identification is a very important application inmachine learning. Due to the increasing amount of available data, traditionalapproaches based on matricization and matrix PCA methods can be difficult toimplement. Moreover, the tensorial approaches are a natural choice, due to themere structure of the databases, for example in the case of color images.Nevertheless, even though various authors proposed factorization strategies fortensors, the size of the considered tensors can pose some serious issues. Whenonly a few features are needed to construct the projection space, there is noneed to compute a SVD on the whole data. Two versions of the tensor Golub-Kahanalgorithm are considered in this manuscript, as an alternative to the classicaluse of the tensor SVD which is based on truncated strategies. In this paper, weconsider the Tensor Tubal Golub Kahan Principal Component Analysis method whichpurpose is to extract the main features of images using the tensor singularvalue decomposition (SVD) based on the tensor cosine product that uses thediscrete cosine transform. This approach is applied for classification and facerecognition and numerical tests show its effectiveness.

A multidimensional principal component analysis via the c-product Golub-Kahan-SVD for classification and face recognition