Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possiblyâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described byâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from theâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- Chris H. Q. Ding, Xiaofeng He
- ICML
- 2004

Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-meansâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- Mark A. Kramer
- 2004

Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method ofâ€¦Â (More)

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Highly Cited

2003

Highly Cited

2003

- RenÃ© Vidal, Yi Ma, S. Shankar Sastry
- IEEE Transactions on Pattern Analysis and Machineâ€¦
- 2003

This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown andâ€¦Â (More)

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Highly Cited

2001

Highly Cited

2001

- Ka Yee Yeung, Walter L. Ruzzo
- Bioinformatics
- 2001

MOTIVATION
There is a great need to develop analytical methodology to analyze and to exploit the information contained in geneâ€¦Â (More)

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Highly Cited

1998

Highly Cited

1998

- Bernhard SchÃ¶lkopf, Alexander J. Smola, Klaus-Robert MÃ¼ller
- Neural Computation
- 1998

A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernelâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.orgâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- Nanda Kambhatla, Todd K. Leen
- Neural Computation
- 1997

Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensionalâ€¦Â (More)

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Highly Cited

1994

Highly Cited

1994

- Pierre Comon
- Signal Processing
- 1994

The independent component analysis (ICA) of a random vector consists of searching for a linear transformation that minimizes theâ€¦Â (More)

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