The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance

Abstract

Principal component analysis (PCA) is linear method for feature extraction that is known as Karhonen Loove method. PCA was first proposed to recognize face by Turk and Pentland, and was also known as eigenface in 1991 [Turk, 1991]. However, PCA has some weaknesses. The first, it cannot capture the simplest invariance of the face image [Arif et al., 2008b] , when this information is not provided in the training data. The last, the result of feature extraction is global structure [Arif, 2008]. The PCA is very simple, has overcome curse of dimensionality problem, this method have been known and expanded by some researchers to recognize face such as Linear Discriminant Analysis (LDA)[Yambor, 2000; A.M. Martinez, 2003; J.H.P.N. Belhumeur 1998], Linear Preserving Projection that known Lapalacianfaces [Cai, 2005; Cai et al, 2006; Kokiopoulou, 2004; X. He et al., 2005], Independent Component Analysis, Kernel Principal Component Analysis [Scholkopf et al., 1998; Sch olkopf 1999], Kernel Linear Discriminant Analysis (KLDA) [Mika, 1999] and maximum feature value selection of nonlinear function based on Kernel PCA [Arif et al., 2008b]. As we know, PCA is dimensionality reduction method based on object appearance by projecting an original ndimensional (row*column) image into k eigenface where k<<n. Although PCA have been developed into some methods, but in some cases, PCA can outperform LDA, LPP and ICA when it uses small sample size.