Description Usage Arguments Value Functions References See Also
These functions find the optimal smoothing parameters α_v,
α_w for the two image directions (v and w) in the FCP_TPA algorithm
based on generalized crossvalidation, which is nested in the tensor power
algorithm. Given a range of possible values of α_v (or
α_w, respectively), the optimum is found by optimizing the GCV
criterion using the function optimize
.
1 2 3  findAlphaVopt(alphaRange, data, u, w, alphaW, OmegaW, GammaV, lambdaV)
findAlphaWopt(alphaRange, data, u, v, alphaV, OmegaV, GammaW, lambdaW)

alphaRange 
A numeric vector with two elements, containing the minimal and maximal value for the smoothing parameter that is to be optimized. 
data 
The tensor containing the data, an array of dimensions 
u, v, w 
The current value of the eigenvectors u_k, v_k, w_k (not
normalized) of dimensions 
GammaV, GammaW 
A matrix of dimension 
lambdaV, 
lambdaW A numeric vector of length 
alphaV, alphaW 
The current value of the smoothing parameter for the
other image direction (α_w for 
OmegaV, 
OmegaW A matrix of dimension 
The optimal α_v (or α_w, respectively), found by optimizing the GCV criterion within the given range of possible values.
findAlphaWopt
:
G. I. Allen (2013), "Multiway Functional Principal Components Analysis", IEEE International Workshop on Computational Advances in MultiSensor Adaptive Processing.
J. Z. Huang, H. Shen and A. Buja (2009), "The Analysis of TwoWay Functional Data Using TwoWay Regularized Singular Value Decomposition". Journal of the American Statistical Association, Vol. 104, No. 488, 1609 – 1620.
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