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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

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

Preface.Introduction.1. Getting Started.2. PCA with More Than Two Variables.3. Scaling of Data.4. Inferential Procedures.5…

Review

1988

Review

1988

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…