Local Marginal Projection and Its Applications

Author

Hong-ben Mao ; Li, Yong Zhi ; Wu, Song Song ; Liu, Fen Xiang

Author_Institution

Coll. of Inf. Sci. &Technol., Nanjing Forestry Univ., Nanjing, China

fYear

2009

fDate

4-6 Nov. 2009

Firstpage

1

Lastpage

4

Abstract

Based on UDP and MFA, we propose a new unsupervised feature extraction algorithm, LMP (Local Marginal Projection), which is built on local quality. It measures the non-local quantities by the nearest sample between two locals. The goal of LMP is to find a projection that can maximize the distance of the sample in the same local and in different locals, in which case, the data can be projected into low-dimension easily. Besides, this projection could deal with the nonlinear and high-dimensional problem. The experiment on ORL and Yale face database shows that LMP algorithm can describe the high-dimensional data and can embed the nonlinear data Swiss-Hole into low-dimension space with a reasonable visual effectively.

Keywords

face recognition; feature extraction; unsupervised learning; MFA; ORL-Yale face database; UDP; face recognition; high-dimensional problem; local marginal projection; low-dimension space; manifold learning algorithm; nonlinear data Swiss-Hole; nonlocal quantity measurement; sample distance maximization; unsupervised feature extraction algorithm; Buildings; Educational institutions; Face recognition; Feature extraction; Forestry; Information science; Neural networks; Scattering; Spatial databases; Visual databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2009. CCPR 2009. Chinese Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-4199-0

Type

conf

DOI

10.1109/CCPR.2009.5344139

Filename

5344139