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Principal component analysis
Known as:
Principle components analysis
, Principle Component Analysis
, Probabilistic principal component analysis
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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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Ali Akansu
Analytica
Apache Spark
Biplot
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Review
2014
Review
2014
A Tutorial on Principal Component Analysis
Jonathon Shlens
arXiv.org
2014
Corpus ID: 2051212
Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly…
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Highly Cited
2004
Highly Cited
2004
K-means clustering via principal component analysis
C. Ding
,
Xiaofeng He
International Conference on Machine Learning
2004
Corpus ID: 11356277
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means…
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Highly Cited
2004
Highly Cited
2004
PRINCIPAL COMPONENT ANALYSIS AND FACTOR ANALYSIS
G. Belle
,
L. Fisher
,
P. Heagerty
,
T. Lumley
Statistical Methods for Biomedical Research
2004
Corpus ID: 118353316
This chapter contains sections titled: Introduction, ICA and PCA, Eigenvectors and Eigenvalues, PCA Applied to Speech Signal…
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Review
2002
Review
2002
A tutorial on Principal Components Analysis
Lindsay I. Smith
2002
Corpus ID: 60161425
Introduction This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a…
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Highly Cited
1998
Highly Cited
1998
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
B. Schölkopf
,
Alex Smola
,
K. Müller
Neural Computation
1998
Corpus ID: 6674407
A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel…
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Highly Cited
1997
Highly Cited
1997
Dimension Reduction by Local Principal Component Analysis
N. Kambhatla
,
T. Leen
Neural Computation
1997
Corpus ID: 147780
Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional…
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Highly Cited
1991
Highly Cited
1991
Nonlinear principal component analysis using autoassociative neural networks
M. Kramer
1991
Corpus ID: 15907287
Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of…
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Highly Cited
1991
Highly Cited
1991
A User's Guide to Principal Components.
J. Lastovicka
,
J. E. Jackson
1991
Corpus ID: 50436960
Preface.Introduction.1. Getting Started.2. PCA with More Than Two Variables.3. Scaling of Data.4. Inferential Procedures.5…
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Review
1988
Review
1988
Principal Component Analysis in Meteorology and Oceanography
R. Preisendorfer
,
C. Mobley
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…
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Highly Cited
1971
Highly Cited
1971
The biplot graphic display of matrices with application to principal component analysis
K. R. Gabriel
1971
Corpus ID: 53465378
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…
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