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EIGENEigen Values (mathematics)
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The higher Eigen values were considered as best representative of system attributes in principal components.
The decay in the Eigen values roughly signifies the amount of information that can be extracted for the unknowns by solving a sub-set of the linear system of equations.
The argument of the first and second eigen values is n, [absolute value of (arg([[lambda].sub.1]))] = [absolute value of (arg([[lambda].sub.2]))] = [pi].
A total of two factors with Eigen values greater than 1.00 were extracted by using varimax rotation.
The Scree plot is showing Eigen values and number of factors that could be retained.
It extracted 5 factors with eigen values greater than 1 and together explained 58.35 percent of the total variance.
The convergence of the Least Mean Square algorithm is inversely proportional to the condition number of t input-signal autocorrelation matrix, depend on the ratio of maximum and minimum Eigen values of this matrix.
Therefore based on patterns of Hessian eigen values in 3D from [20], [[lambda].sub.1] can be approximately 0 and [[lambda].sub.2] and [[lambda].sub.3] are negative numbers with absolutely high values as shown in the equation (3).
Results showed that the first three principal components with the eigen values greater than one (>1.0) represent 93.4% of the total variability, suggesting that three principal components effectively describe the disparity in the data set.
Specifically, a matrix of pair-wise correlations among indicators is collapsed into eigenvectors, which, in turn, are sorted in descending order of their corresponding Eigen values (Vialle et al.
This is achieved by neglecting the non-autonomous or constant parts of the system of equations (1) to (4) and finding the eigen values of its system matrix (Centea et al., (2001)).
The Eigen values of all the components, the variance explained by each component and the cumulative variance were calculated by SPSS and are presented in Table 2.