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A vector whose direction is unchanged by a given transformation and whose magnitude is changed by a factor corresponding to that vector's eigenvalue. In quantum mechanics, the transformations involved are operators corresponding to a physical system's observables. The eigenvectors correspond to possible states of the system, and the eigenvalues to possible observed values.

[Partial translation of German Eigenvektor : eigen-, characteristic; see eigenvalue + Vektor, vector.]
American Heritage® Dictionary of the English Language, Fifth Edition. Copyright © 2016 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved.


(Mathematics) maths physics a vector x satisfying an equation Ax = λx, where A is a square matrix and λ is a constant
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014
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[15] indicates that by precoding the transmitted signals along the weakest eigenvector of the unintended user's channel covariance matrix, the inter-user interference can be effectively suppressed.
And the optimal projection space X can be determined by the spread of eigenvector Y.
However, there are more ways for how to calculate the eigenvector components.
Similarly, "2-reach centrality" was used to explore the proportion of nodes that can reach a given node in 2 steps or less while "eigenvector centrality" was used to measure the importance of a node depending on the importance of its neighbours.
(3) Extrapolate for source eigenvector [[bar.[PHI]].sub.Z] via Nystrom approximation by (6).
The classification results of the control group and treatment group were determined using the first eigenvector of the LDA algorithm and the MPA and MNA feature values.
Then, these two operators have common eigenstates in [H.sub.q], say |[[psi].sub.i](q)>, but there is no eigenvector in [H.sub.q] [cross product] [H.sub.t] that can simultaneously satisfy both Schrodinger equations (3) and (5).
Form the column image vectors and find out the all scatter matrix values, and then with the use of this scatter values, find the eigenvalues and eigenvector matrix.
where the signal eigenvector matrix [E.sub.s] = [[e.sub.0], ..., [e.sub.M-1]] contains M eigenvectors that span the signal subspace of the correlation matrix, the noise eigenvector matrix [E.sub.N] = [[e.sub.M], ..., [g.sub.N-1]] indicates N-M eigenvectors spanning the noise subspace of the correlation matrix, and [[lambda].sub.n] denotes the n-th eigenvalues of [R.sub.s].
[20] extracted the eigenvector of RGB color moment and the Munsell color moment from the images.
What is needed to be noticed is that the constrained normalized cuts problem [15] makes SCACS algorithm solve the generalized eigenvector problem twice.