eigenvalue
- noun
- /ˈaɪɡənˌvælju/
- Specialized
- In linear algebra, an eigenvalue represents a scalar that scales a corresponding eigenvector when a transformation is applied.
- factors and eigenvalues
- eigenvalues and eigenvectors
- minimum eigenvalue
Examples
-
The Kaiser criterion recommends that all factors with eigenvalues greater than one be considered.
Academic text (2008) -
The PCA showed that the first factor with an eigenvalue of 4.68 accounted for 78% of the variance.
Academic text (2013) -
This would have an eigenvalue smaller than that.
-
However, in each case, every factor had an eigenvalue of 1.0 or larger.
Academic text (1996) -
None of the factors after the first one had eigenvalues greater than one.
Academic text (1996) -
The initial factor extraction, using eigenvalue = 1.0 as criteria, resulted in a 10-factor model.
Academic text (1992) -
In practice, we can leave out insignificant eigenvalues and still get reasonably good results.
Academic text (2017) -
The significant loadings on the two factors, eigenvalues, and percentage of variance are reported in Table 1.
Academic text (1996) -
For the compensatory consumption scale, EFA revealed three factors with Eigenvalues greater than 1.0.
Academic text (2008) -
The analysis yielded three factors with eigenvalues higher than 1.0, which explained 53.27% of the variance.
Academic text (2008)
Synonyms
A number that makes a square matrix have determinant zero when subtracted from its diagonal entries
Surface Forms
Morphology
Etymology
Eigenvalue comes from the German part eigen, which means 'own' or 'characteristic', plus value, meaning 'number'. So an eigenvalue is a special number that 'belongs' to a matrix and shows an important feature of that matrix.