Results 291 to 300 of about 249,692 (322)

DySCo: A general framework for dynamic functional connectivity. [PDF]

open access: yesPLoS Comput Biol
Alteriis G   +6 more
europepmc   +1 more source

On Eigenvector Bounds

BIT Numerical Mathematics, 2003
The authors investigate the conditions under which it is possible to estimate and compute error bounds on a computed eigenvector of a finite matrix. It is shown that nontrivial error bounds on an eigenvector are computable if and only if its geometric multiplicity is one. They also provide an algorithm for the computation of these error bounds and show
Rump, Siegfried M., Zemke, Jens-Peter M.
openaire   +1 more source

Extended eigenvalue–eigenvector method

Statistics & Probability Letters, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kataria, K. K., Khandakar, M.
openaire   +2 more sources

Eigenvalues and eigenvectors

2007
Eigenvalues and the associated eigenvectors of an endomorphism of a vector space are defined and studied, as is the spectrum of an endomorphism. The characteristic polynomial of a matrix is considered and used to define the characteristic polynomial of the endomorphism of a finitely-generated vector space.
openaire   +1 more source

Eigenvector radiosity

2009
Radiative flux transfer between Lambertian surfaces can be described in terms of linear resistive networks with voltage sources. This thesis examines how these "radiative transfer networks" provide a physical interpretation for the eigenvalues and eigenvectors of form factor matrices. This leads to a novel approach to photorealistic image synthesis and
openaire   +1 more source

Eigenvectors and Eigenvalues

1997
Gaussian elimination plays a fundamental role in solving a system Ax = b of linear equations. In order to solve a system of linear equations, Gaussian elimination reduces the augmented matrix to a (reduced) row-echelon form by using elementary row operations that preserve row and null spaces.
Jin Ho Kwak, Sungpyo Hong
openaire   +1 more source

An eigenvector variability plot

2009
Summary: Principal components analysis is perhaps the most widely used method for exploring multivariate data. We propose a variability plot composed of measures on the stability of each eigenvector over samples as a data exploration tool. We also show that this variability measure gives a good measure on the intersample variability of eigenvectors ...
Tu, I. P., Chen, H., Chen, X.
openaire   +1 more source

Eigenvalues and Eigenvectors

1986
T. S. Blyth, E. F. Robertson
openaire   +1 more source

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