One of the axes representing the projection of varience resulting from principal component analysis.

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Highly Cited

2011

Highly Cited

2011

- Xinghao Ding, Lihan He, Lawrence Carin
- IEEE Transactions on Image Processing
- 2011

A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and sparse components, assuming the observedâ€¦Â (More)

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Highly Cited

2011

Highly Cited

2011

- Edo Liberty
- 2011

l=1 Ïƒlulv T l (1) âˆ€ l Ïƒl âˆˆ R, Ïƒl â‰¥ 0 (2) âˆ€ l, l ã€ˆul, ulâ€²ã€‰ = ã€ˆvl, vlâ€²ã€‰ = Î´(l, l) (3) To prove this consider the matrix AA âˆˆ R. Setâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- Johan Bollen, Herbert Van de Sompel, Aric A. Hagberg, Ryan Chute
- PloS one
- 2009

BACKGROUND
The impact of scientific publications has traditionally been expressed in terms of citation counts. Howeverâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- Nojun Kwak
- IEEE Transactions on Pattern Analysis and Machineâ€¦
- 2008

A method of principal component analysis (PCA) based on a new L1-norm optimization technique is proposed. Unlike conventional PCAâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos
- IEEE Transactions on Neural Networks
- 2008

This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objectsâ€¦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

- 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

Mei-Ling Shyu1âˆ—, Shu-Ching Chen2, Kanoksri Sarinnapakorn1, LiWu Chang3 1Department of Electrical and Computer Engineeringâ€¦Â (More)

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Highly Cited

2001

Highly Cited

2001

- William W. Hsieh
- 2001

Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonlinearly generalizes theâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- Michael E. Tipping, Christopher M. Bishop, Peter Dayan, Bernhard SchÃ¶lkopf, Alexander J. Smola, Klaus-Robert MÃ¼ller
- 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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