FPCA_2D_score_fast: Two Dimensional Functional Principal Component Analysis

In FPCA2D: Two Dimensional Functional Principal Component Analysis

Description

Calcualte the two dimensional functional principal component scores by using Fourier Basis

Usage

FPCA_2D_score_fast(X)

Arguments

X	X is the input three dimensional array. The first two dimensions are the dimension of each input image. All the inputs images are organized as the third dimension of the input data array. All the image should be scaled to the rage from 0 to 1 before running this function.

Details

Calcualte the two dimensional functional principal component scores by using Fourier Basis

Value

eigen_value	The eigen value can be used to calcualte the proportion of variance that each FPC score can explain.
FPC_score	The output FPC scores.
Eigen_vector	The eigen_vector represents the directions of the lienar transformation in the functional domain.

References

Lin N, Jiang J, Guo S, Xiong M (2015) Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis. PLoS ONE 10(7): e0132945. doi:10.1371/journal.pone.0132945

Examples

   A = array(sample(seq(0,1,0.001),300),dim=c(10,10,3))
   rlt = FPCA_2D_score_fast(A)

FPCA2D documentation built on May 2, 2019, 12:36 p.m.