With the availability of high-density genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences about population relationships from the results of PCA, especially when admixed individuals are ...

Usually, principal components analysis is carried out by calculating the eigenvalues and eigenvectors of the correlation matrix. With N cases and P variables, if we write X for the N × P matrix which has been standardised so that columns have zero mean and unit standard deviation, we find the eigenvalues and eigenvectors of the P × P matrix X.X (which is N or (N − 1) times the correlation matri...

© 2007 Wiley Periodicals, Inc. • Published online 22 January 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/asi.20537 three fundamental components: a set of documents, a set of posed information needs, and a set of relevance judgments. Relevance judgments are the collections of documents that should be retrieved for each information need, and a posed information need is a...

We use principal component analysis (PCA) to reduce the dimensionality of features of video frames for the purpose of content description. This low dimensional description makes practical the direct use of all the frames of a video sequence in later analysis. The PCA representation circumvents or eliminates several of the stumbling blocks in current analysis methods, and makes new analyses feas...

We report on an evaluation study of a ship classi er based on the Principal Components Analysis (PCA). A set of ship pro les are used to build a covariance matrix which is diagonalized using the Karhunen-Lo eve transform. A subset of the principal components corresponding to the highest eigenvalues are selected as the ship features space. The recognition process consists in projecting a pro le ...

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the proje...

A principal components model for analyzing tree -ring data is presented which allows one to examine site heterogeneity and to compose chronologies of a new kind in a conceptually unified and computationally efficient manner. Using this model, one can develop chronologies that correlate better with local climate data than the standard chronology for a site and which can be tested for time stabil...

BACKGROUND Airway inflammation in COPD can be measured using biomarkers such as induced sputum and Fe(NO). This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal components analysis (PCA). SUBJECTS AND METHODS In 127 COPD patients (mean FEV1 61%), pulmonary function, Fe(NO), plasma CRP and TNF-alpha, spu...

The goal of principal components analysis (PCA) is to find principal components in accordance with maximum variance of a data matrix. However, it has been shown recently that such variance-based principal components may not adequately represent image quality. As a result, a modified PCA approach based on maximization of SNR was proposed. Called maximum noise fraction (MNF) transformation or noi...