Principal Component Analysis

Known as: Analysis, Principal Component, Principal Component Analyses, PCA 
A vector space transform used to reduce the dimensionality in a dataset while retaining those characteristics of the dataset that contribute most to… (More)
National Institutes of Health

Papers overview

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Review
2017
Review
2017
INTRODUCTION A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition… (More)
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Highly Cited
2009
Highly Cited
2009
Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by… (More)
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Highly Cited
2009
Highly Cited
2009
We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We… (More)
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Highly Cited
2008
Highly Cited
2008
Several measurement techniques used in the life sciences gather data for many more variables per sample than the typical number… (More)
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Highly Cited
2006
Highly Cited
2006
Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can… (More)
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Highly Cited
2004
Highly Cited
2004
A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard… (More)
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Highly Cited
2002
Highly Cited
2002
This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of… (More)
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Highly Cited
2000
Highly Cited
2000
Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene… (More)
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Highly Cited
1999
Highly Cited
1999
Principal component analysis (PCA) is one of the most popular techniques for processing, compressing, and visualizing data… (More)
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Highly Cited
1998
Highly Cited
1998
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific… (More)
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