A series of float values each of which represents proportion of variance for each principal component.

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Highly Cited

2003

Highly Cited

2003

We determine analytically the modulus of the second eigenvalue for the web hyperlink matrix used by Google for computing PageRankâ€¦Â (More)

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Highly Cited

2002

Highly Cited

2002

For the nonlinear eigenvalue problem T (Î»)x = 0 we propose an iterative projection method for computing a few eigenvalues closeâ€¦Â (More)

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Highly Cited

2002

Highly Cited

2002

- Mark A. Girolami
- Neural Computation
- 2002

Kernel principal component analysis has been introduced as a method of extracting a set of orthonormal nonlinear features fromâ€¦Â (More)

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Highly Cited

1999

Highly Cited

1999

- coise TisseuryJuly
- 1999

We develop normwise backward errors and condition numbers for the polynomial eigenvalue problem. The standard way of dealing withâ€¦Â (More)

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Highly Cited

1996

Highly Cited

1996

In this paper an analogue of the Schwarz alternating method is considered to nd a minimal eigenvalue and its correspondingâ€¦Â (More)

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Highly Cited

1994

Highly Cited

1994

The Lanczos process is a well known technique for computing a few, say k, eigenvalues and associated eigenvectors of a largeâ€¦Â (More)

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Highly Cited

1994

Highly Cited

1994

The problem of separating n linearly superimposed uncorrelated signals and determing their mixing coeecents is reduced to anâ€¦Â (More)

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Highly Cited

1988

Highly Cited

1988

- Michael L Overton 'f
- 1988

An important optimization problem that arises in control is to minimize o(x), the largest eigenvalue (in magnitude) of aâ€¦Â (More)

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Highly Cited

1987

Highly Cited

1987

- James Demmel, Jack J. Dongarra, +4 authors Danny C. Sorensen
- 1987

In this paper we describe block algorithms for the reduction of a real symmetric matrix to tridiagonal form and for the reductionâ€¦Â (More)

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Highly Cited

1950

Highly Cited

1950

The present investigation designs a systematic method for finding the latent roots and the principal axes of a matrix, withoutâ€¦Â (More)

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