Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"

Author

Deng, Weihong ; Hu, Jiani ; Guo, Jun ; Zhang, Honggang ; Zhang, Chuang

Author_Institution

Sch. of Inf. Eng., Beijing Univ. of Posts & Telecommun., Beijing

Volume

30

Issue

8

fYear

2008

Firstpage

1503

Lastpage

1504

Abstract

In (Yang et al., 2007), UDP is proposed to address the limitation of LPP for the clustering and classification tasks. In this communication, we show that the basic ideas of UDP and LPP are identical. In particular, UDP is just a simplified version of LPP on the assumption that the local density is uniform.

Keywords

authorisation; face recognition; learning (artificial intelligence); matrix algebra; UDP; classification tasks; clustering tasks; face biometrics; locality preserving projection; palm biometrics; unsupervised discriminant projection; Biometrics; Laplace equations; Learning systems; Linear approximation; Scattering; Solid modeling; Face and gesture recognition; Feature evaluation and selection; Statistical; Algorithms; Artificial Intelligence; Biometry; Computer Simulation; Discriminant Analysis; Face; Hand; Humans; Image Interpretation, Computer-Assisted; Models, Biological; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2007.70783

Filename

4359377