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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… Expand
National Institutes of Health

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2016
Review
2016
Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique… Expand
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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… Expand
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Highly Cited
2008
Highly Cited
2008
Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to… Expand
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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… Expand
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Review
2006
Review
2006
Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the… Expand
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Highly Cited
2002
Highly Cited
2002
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
Highly Cited
1999
Highly Cited
1999
Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based… Expand
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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… Expand
Highly Cited
1995
Highly Cited
1995
The purpose of the present study was to revise the Barratt Impulsiveness Scale Version 10 (BIS-10), identify the factor structure… Expand
Highly Cited
1964
Highly Cited
1964
The paper provides various interpretations of principal components in the analysis of multiple measurements. A number of… Expand