Principal Component Analysis
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Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique… Expand We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We… Expand Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to… Expand Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can… Expand Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the… Expand The purpose of this research is twofold. First is to extend the work of Smith (1992, 1996) and Smith and Miao (1991, 1994) in… Expand Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based… Expand Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific… Expand The purpose of the present study was to revise the Barratt Impulsiveness Scale Version 10 (BIS-10), identify the factor structure… Expand The paper provides various interpretations of principal components in the analysis of multiple measurements. A number of… Expand