View source: R/multiscaleSVDxpts.R
smoothAppGradCCA | R Documentation |
This implements a sparse and graph-regularized version of CCA based on the AppGrad style of implementation by Ma, Lu and Foster, 2015.
smoothAppGradCCA(
x,
y,
smoox = NA,
smooy = NA,
sparsenessQuantile = 0.5,
positivity = "either",
k = 2,
iterations = 10,
stochastic = NA,
initialization = "randxy",
verbose = FALSE
)
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 |
verbose |
boolean option |
list with matrices each of size p or q by k
Avants BB
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)
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