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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… Expand
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Papers overview

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Highly Cited
2011
Highly Cited
2011
Principal Component Analysis
  • Ian T. Jolliffe
  • International Encyclopedia of Statistical Science
  • 2011
    • Corpus ID: 27917863
 
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Highly Cited
2010
Highly Cited
2010
Two-stage image denoising by principal component analysis with local pixel grouping
This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping… Expand
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Highly Cited
2009
Highly Cited
2009
Principal Component Analysis
  • Heng Tao Shen
  • Encyclopedia of Database Systems
  • 2009
    • Corpus ID: 2534141
Ink jet printing apparatus and method using timing control of electronic waveforms for variable gray scale printing while… Expand
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Highly Cited
2008
Highly Cited
2008
Sparse principal component analysis via regularized low rank matrix approximation
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout… Expand
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Highly Cited
2004
Highly Cited
2004
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means… Expand
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Highly Cited
1994
Highly Cited
1994
Independent component analysis, A new concept?
Abstract The independent component analysis (ICA) of a random vector consists of searching for a linear transformation that… Expand
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Highly Cited
1991
Highly Cited
1991
Nonlinear principal component analysis using autoassociative neural networks
Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of… Expand
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Highly Cited
1989
Highly Cited
1989
Neural networks and principal component analysis: Learning from examples without local minima
Abstract We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization… Expand
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Highly Cited
1987
Highly Cited
1987
Principal Component Analysis
Abstract Principal component analysis of a data matrix extracts the dominant patterns in the matrix in terms of a complementary… Expand
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Highly Cited
1971
Highly Cited
1971
The biplot graphic display of matrices with application to principal component analysis
SUMMARY Any matrix of rank two can be displayed as a biplot which consists of a vector for each row and a vector for each column… Expand
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