# 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

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Review
2014
Review
2014
Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly…
Highly Cited
2004
Highly Cited
2004
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means…
Highly Cited
2004
Highly Cited
2004
• 2004
• Corpus ID: 118353316
This chapter contains sections titled: Introduction, ICA and PCA, Eigenvectors and Eigenvalues, PCA Applied to Speech Signal…
Review
2002
Review
2002
Introduction This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a…
Highly Cited
1998
Highly Cited
1998
A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel…
Highly Cited
1997
Highly Cited
1997
Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional…
Highly Cited
1991
Highly Cited
1991
Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of…
Highly Cited
1991
Highly Cited
1991
• 1991
• Corpus ID: 50436960
Preface.Introduction.1. Getting Started.2. PCA with More Than Two Variables.3. Scaling of Data.4. Inferential Procedures.5…
Review
1988
Review
1988
• 1988
• Corpus ID: 122456032
List of figures. List of tables. 1. Introduction. An overview of principal component analysis (PCA). Outline of the book. A brief…
Highly Cited
1971
Highly Cited
1971
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…