The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance
Mauridhi Hery, Puspitaningrum Diah, I Ketut Eddy Purnama, Arif Muntas · InTech eBooks · 2012
IntroductionPrincipal 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;