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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… Expand

Highly Cited

2009

Highly Cited

2009

The Karhunen-Lo eve basis functions, more frequently referred to as principal components or empirical orthogonal functions (EOFs… Expand

Highly Cited

2008

Highly Cited

2008

Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout… Expand

Highly Cited

2004

Highly Cited

2004

Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means… Expand

Highly Cited

1994

Highly Cited

1994

Abstract The independent component analysis (ICA) of a random vector consists of searching for a linear transformation that… Expand

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… Expand

Highly Cited

1989

Highly Cited

1989

Abstract We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization… Expand

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… Expand

Highly Cited

1987

Highly Cited

1987

Principal Component Analysis (PCA) is a multivariate exploratory analysis method, useful to separate systematic variation from… Expand

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… Expand