smoothAppGradCCA: cca via sparse smooth matrix prediction

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Description

This implements a sparse and graph-regularized version of CCA based on the AppGrad style of implementation by Ma, Lu and Foster, 2015.

Usage

smoothAppGradCCA(
  x,
  y,
  smoox = NA,
  smooy = NA,
  sparsenessQuantile = 0.5,
  positivity = "either",
  k = 2,
  iterations = 10,
  stochastic = NA,
  initialization = "randxy",
  verbose = FALSE
)

Arguments

x	input view 1 matrix
y	input view 2 matrix
smoox	smoothingMatrix for x
smooy	smoothingMatrix for y
sparsenessQuantile	quantile to control sparseness - higher is sparser
positivity	restrict to positive or negative solution (beta) weights. choices are positive, negative or either as expressed as a string.
k	number of basis vectors to compute
iterations	number of gradient descent iterations
stochastic	size of subset to use for stocastic gradient descent
initialization	type of initialization, currently only supports a character randxy
verbose	boolean option

Value

list with matrices each of size p or q by k

Author(s)

Avants BB

Examples

mat<-replicate(100, rnorm(20))
mat2<-replicate(100, rnorm(20))
mat<-scale(mat)
mat2<-scale(mat2)
wt<-0.666
mat3<-mat*wt+mat2*(1-wt)
jj = smoothAppGradCCA( mat, mat3 )

neuroconductor-devel/ANTsR documentation built on April 1, 2021, 1:02 p.m.