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Principal component analysis

Known as: Principle components analysis, Principle Component Analysis, Probabilistic principal component analysis 
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2010
2010
A New Method for Maintenance Management Employing Principal Component Analysis
This paper presents a simple graphic method for detecting and classifying faults in point mechanisms based on the study of some… 
Highly Cited
2009
Highly Cited
2009
Reduced‐order modeling of parameterized PDEs using time–space‐parameter principal component analysis
This paper presents a methodology for constructing low‐order surrogate models of finite element/finite volume discrete solutions… 
Highly Cited
2008
Highly Cited
2008
High-dimensional analysis of semidefinite relaxations for sparse principal components
In problem of sparse principal components analysis (SPCA), the goal is to use n i.i.d. samples to estimate the leading… 
Review
2005
Review
2005
A review of principal component analysis and its applications to color technology
Principal component analysis, abbreviated PCA, has been an important and useful mathematical tool in color technology since the… 
Highly Cited
2005
Highly Cited
2005
Coherency identification in power systems through principal component analysis
In this letter, a new technique to identify coherent generators in large interconnected power system using measurements of… 
Highly Cited
2005
Highly Cited
2005
Gene expression A web-based tool for principal component and significance analysis of microarray data
Summary: We have developed a program for microarray data analysis, which features the false discovery rate for testing… 
Highly Cited
2004
Highly Cited
2004
Source apportionment of ambient non-methane hydrocarbons in Hong Kong : Application of a principal component analysis / absolute principal component scores ( PCA / APCS ) receptor model
Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source… 
Review
2003
Review
2003
A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation , Discussion and Singular Value Decomposition
Principal component analysis (PCA) is a mainstay of modern data analysis a black box that is widely used but poorly understood… 
Highly Cited
2002
Highly Cited
2002
Principal Component Analysis for Dimension Reduction in Massive Distributed Data Sets ∗
We describe a new method for computing a global principal component analysis (PCA) for the purpose of dimension reduction in data… 
Highly Cited
1987
Highly Cited
1987
Application of principal components analysis to change detection
: Principal components analysis (PCA) has been applied for land-cover change detection with multitemporal Landsat Multispectral… 
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