Noise adjusted version of generalized principal component analysis

Authors

MOJTABA AMINI-OMAM
FARAH TORKAMANI AZAR

DOI

10.3906/elk-1303-151

Abstract

Principal component analysis (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 (GPCA) could be applied to images in two dimensional spaces. Both schemes do not consider the noisy case of signals. Noise adjusted PCA (NAPCA) tries to find new coordinates for signal representation based on signal to noise ratio (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

First Page

50

Last Page

60

Recommended Citation

AMINI-OMAM, MOJTABA and AZAR, FARAH TORKAMANI (2016) "Noise adjusted version of generalized principal component analysis," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 24: No. 1, Article 5. https://doi.org/10.3906/elk-1303-151
Available at: https://journals.tubitak.gov.tr/elektrik/vol24/iss1/5