Examples of 'eigenvector' in a sentence
Meaning of "eigenvector"
eigenvector (noun) - in mathematics, a vector that does not change its direction during a given linear transformation
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- given a linear transformation A, a vector x such that Ax=λx for some scalar λ
- specifically, given a matrix A, the eigenvector of the transformation "left-side multiplication by A"
How to use "eigenvector" in a sentence
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eigenvector
An ordinary eigenvector is a special case of a generalized eigenvector.
The second matrix provides eigenvector information.
Is the eigenvector between station j and the principal component.
I can not believe an eigenvector is actually useful.
There are several equivalent ways to define an ordinary eigenvector.
The vector converges to an eigenvector of the largest eigenvalue.
Whose solution is the space spanned by the eigenvector.
Notice that the vector x is an eigenvector to the corresponding eigenvalue λ.
The eigenvector sequences are expressed as the corresponding similarity matrices.
One of these measures is termed eigenvector centrality.
The eigenvector is strict positivity.
Katz centrality can be viewed as a variant of eigenvector centrality.
Called the eigenvector residual.
For each of the eigenvalues calculated we have an individual eigenvector.
In particular degree centrality and eigenvector centrality are compared.
See also
A unit eigenvector is obtained by normalizing it.
Using the adjacency matrix to find eigenvector centrality.
Principal eigenvector of the normalized link matrix of the web.
An eigenfunction is a type of eigenvector.
An eigenvector specifies the direction.
This will quickly converge to the eigenvector of the closest eigenvalue to μ.
The eigenvector associated with the smallest singular value or eigenvalue.
The blue vector changes direction and hence is not an eigenvector.
The principal eigenvector is used to measure the centrality of its vertices.
Is different from the overall transfer function with an unnormalized eigenvector.
A has a right eigenvector v with eigenvalue r whose components are all positive.
The corresponding eigenvalues will describe the amount of variation along each eigenvector.
The solution is the eigenvector of associated with the smallest eigenvalue.
And one of the angles we just saw and emphasized is the dominant eigenvector angle.
The eigenvector is the property of the system that you are looking at.
It then calculates the principal eigenvector of the resulting modified adjacency matrix.
Eigenvector of a matrix.
Identifying edges through use of an eigenvector that corresponds to the selected eigenvalue.
And we call their scaling factors the eigenvalues associated with this transformation and that eigenvector.
An eigenfunction is a type of eigenvector that is both a unique characteristic of a parameter and a function.
Each eigenvalue λj corresponds to an orthogonal basis eigenvector Xj.
The components of this eigenvector give the relative abundance of each sequence at equilibrium.
Any vector that satisfies this right here is called an eigenvector for the transformation T.
The first eigenvector can be interpolated by various methods to obtain a model for stem taper.
Dimensionality reduction yields one eigenvector for each one of the training speakers.
Eigenvector Eigenvector centrality is a measure of the influence of a node in a network.
So obviously this is the eigenvector that dominates the spread of the data points.
We can also reduce the dimensionality through the use of multilevel dominant eigenvector estimation MDEE.
Each resulting eigenvector with a complex number for each antenna element constitutes an eigenbeam.
Each eigenvalue λj corresponds to an orthogonal basis eigenvector X j.
The fourth is the angle about the eigenvector that separates the two sets of coordinates.
The eigenvector can be computed by using sparse SVD algorithms.
The exact calculation of the Eigenvector is determined only in specific cases.
The eigenvector corresponding to the least eigenvalue of AA allows to directly obtaining the solution.
The top ten men with highest Eigenvector centrality scores in the network of Socrates.
