A kernel based non-negative matrix factorization

Author

Jun, Yu ; Jintao, Meng ; Xiaoxu, Lu

Author_Institution

Zhengzhou Inst. of Aeronaut. Ind. Manage., Zhengzhou, China

Volume

1

fYear

2010

fDate

28-31 Aug. 2010

Firstpage

376

Lastpage

379

Abstract

In this paper, we extend the original projective non-negative matrix factorization (P-NMF) to kernel P-NMF (KP-NMF). The advantages of KP-NMF over P-NMF are:1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with non-linear data well; 3) it can process data with negative values by using some specific kernel functions. Thus, KP-NMF is more general than P-NMF. Experimental on ORL datasets and the UMIST face database results show that KP-NMF derives bases which are somewhat better suitable for localized representation than KP-NMF.

Keywords

face recognition; hidden feature removal; matrix decomposition; operating system kernels; ORL datasets; UMIST face database; hidden feature extraction; kernel based nonnegative matrix factorization; kernel-induced nonlinear mappings;; localized representation; nonlinear data; Accuracy; Classification algorithms; Databases; Face; Feature extraction; Kernel; Principal component analysis; dimensionality reduction; kernel; nonlinear mappings; projective non-negative matrix factorization;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing (IITA-GRS), 2010 Second IITA International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-8514-7

Type

conf

DOI

10.1109/IITA-GRS.2010.5603062

Filename

5603062