curvilinear: Curvilinear algorithm with r0 joint components

In singR: Simultaneous Non-Gaussian Component Analysis

Curvilinear algorithm with r0 joint components

Description

The curvilinear algorithm is modified from Wen and Yin paper.

Usage

curvilinear(
  Ux,
  Uy,
  xData,
  yData,
  invLx,
  invLy,
  rho,
  tau = 0.01,
  alpha = 0.8,
  maxiter = 1000,
  tol = 1e-06,
  rj
)

Arguments

Ux	Matrix with n.comp x n, initial value of Ux, comes from greedyMatch.
Uy	Matrix with n.comp x n, initial value of Uy, comes from greedyMatch.
xData	matrix with n x px, Xw = Lx %*% Xc.
yData	matrix with n x py, Yw = Ly %*% Yc.
invLx	Inverse matrix of Lx, matrix n x n.
invLy	Inverse matrix of Ly, matrix n x n.
rho	the weight parameter of matching relative to non-gaussianity.
tau	initial step size, default value is 0.01
alpha	controls weighting of skewness and kurtosis. Default value is 0.8, which corresponds to the Jarque-Bera test statistic with 0.8 weighting on squared skewness and 0.2 on squared kurtosis.
maxiter	default value is 1000
tol	the threshold of change in Ux and Uy to stop the curvlinear function
rj	the joint rank, comes from greedyMatch.

Value

a list of matrices:

Ux

Optimized Ux with matrix n.comp x n.

Uy

Optimized Uy with matrix n.comp x n.

tau

step size

iter

number of iterations.

error

PMSE(Ux,Uxnew)+PMSE(Uy,Uynew)

obj

Objective Function value

singR documentation built on May 29, 2024, 7:30 a.m.