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Varimax is so called because it maximizes the sum of the variances of the squared loadings (squared correlations between variables and factors). Preserving orthogonality requires that it is a rotation that leaves the sub-space invariant.
A varimax solution yields results which make it as easy as possible to identify each variable with a single factor. This is the most common orthogonal rotation option. [2] Quartimax rotation is an orthogonal rotation that maximizes the squared loadings for each variable rather than each factor.
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For example, in 2-space n = 2, a rotation by angle θ has eigenvalues λ = e iθ and λ = e −iθ, so there is no axis of rotation except when θ = 0, the case of the null rotation. In 3-space n = 3 , the axis of a non-null proper rotation is always a unique line, and a rotation around this axis by angle θ has eigenvalues λ = 1, e iθ , e ...
In rotation recovery, you find a rotation between point clouds by subtracting the mean from both and then taking the outer product of the two arrays. That is, is the outer product between them, and you want to find the rotation that registers them. It turns out that the rotation you want is the one that maximizes the trace of this outer product.
Varimax (orthogonal) and PROMAX (oblique) rotation of factor loadings; ... Excel and SPSS file formats, and produces exportable graphics for all statistical analyses ...
In statistics and signal processing, the method of empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data.
In order to reduce mixture effects and to improve the physical interpretation, Groth and Ghil (2011) have proposed a subsequent VARIMAX rotation of the spatio-temporal EOFs (ST-EOFs) of the M-SSA. To avoid a loss of spectral properties (Plaut and Vautard 1994), they have introduced a slight modification of the common VARIMAX rotation that does ...