Noise adjusted version of generalized principal component analysis

Authors : Mojtaba AMINI-OMAM, Farah Torkamani AZAR

Pages : 50-60

View : 8 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Principal component analysis insert ignore into journalissuearticles values(PCA); is a well-known tool in image processing, especially in dimension reduction schemes. Since using PCA is based on the vector representation of the image, the spatial locality of pixels in the image is not considered. However, generalized PCA insert ignore into journalissuearticles values(GPCA); could be applied to images in two dimensional spaces. Both schemes do not consider the noisy case of signals. Noise adjusted PCA insert ignore into journalissuearticles values(NAPCA); tries to find new coordinates for signal representation based on signal to noise ratio insert ignore into journalissuearticles values(SNR); maximization. In this paper we generalized noise adjusted GPCA to benefit the advantage of GPCA and SNR maximization case of NAPCA in two dimensional spaces. The experimental results on the huge databases show its reliability.
Keywords : Principal component analysis, generalized principal component analysis, signal to noise ratio improvement, noise adjusted principal component analysis