Candidate gene association studies often utilize one single nucleotide polymorphism (SNP) for analysis, with an initial report typically not being replicated by subsequent studies. The failure to replicate may result from incomplete or poor identification of disease-related variants or haplotypes, possibly due to naive SNP selection. A method for identification of linkage disequilibrium (LD) gr...

As with any other multivariate statistical method, Principal Components Analysis is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. This paper introduces a powerful approach to improve PCA: robust fuzzy PCA algorithm (FPCA). The matrix data is fuzzified, thus di...

An asynchronous phase-shifting method based on principal component analysis (PCA) is presented. No restrictions about the background, modulation, and phase shifts are necessary. The presented method is very fast and needs very low computational requirements, so it can be used with very large images and/or very large image sets. The method is based on obtaining two quadrature signals by the PCA ...

Face recognition is one of the Biometric characteristics for person identification. In this paper, Face recognition is done using two feature extraction techniques PCA (Principal Component Analysis) and MPCA (Modular Principal Component Analysis). PCA is a linear projection method in which dimensionality reduction is applied to the original image space. MPCA is an improved version of PCA in whi...

Electrical discharge machining (EDM) is one of the most extensively used non-traditional machining processes having multiple performance characteristics, some of which are usually correlated. So, ideally, use of principal component analysis (PCA)-based approaches that take into account the possible correlations between the responses are suitable for optimization of EDM process. A recently repor...

Principal Component Analysis (PCA) is a very well known statistical tool. Kernel PCA is a nonlinear extension to PCA based on the kernel paradigm. In this paper we characterize the projections found by Kernel PCA from a information theoretic perspective. We prove that Kernel PCA provides optimum entropy projections in the input space when the Gaussian kernel is used for the mapping and a sample...

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover n...

In this article, a face recognition system using the Principal Component Analysis (PCA) algorithm was implemented. The algorithm is based on an eigenfaces approach which represents a PCA method in which a small set of significant features are used to describe the variation between face images. Experimental results for different numbers of eigenfaces are shown to verify the viability of the prop...

MOTIVATION Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology (GO) or KEGG Pathway databases. We propose a new method for gene set analysis that is based on principal component analysis (PCA) of genes expression values in the gene set. PCA is an effective method for reducing high dimensionality and capture variat...

Kernel Principal Component Analysis (KPCA) is a dimension reduction method that is closely related to Principal Component Analysis (PCA). This report gives an overview of kernel PCA and presents an implementation of the method in MATLAB. The implemented method is tested in a transductive setting on two data bases: Iris data and sugar data. Two methods for labeling data points are considered, th...