RKUM (version 0.1.1.1)

rkcco: Robust kernel cross-covariance opetator

Description

# A function

Usage

rkcco(X, Y, lossfu = "Huber", kernel = "rbfdot", gamma = 1e-05)

Arguments

X

a data matrix index by row

Y

a data matrix index by row

lossfu

a loss function: square, Hampel's or Huber's loss

kernel

a positive definite kernel

gamma

the hyper-parameters

Value

rkcmx

Robust kernel center matrix of X dataset

rkcmy

Robust kernel center matrix of Y dataset

rkcmx

Robust kernel covariacne operator of X dataset

rkcmy

Robust kernel covariacne operator of Y dataset

rkcmx

Robust kernel cross-covariacne operator of X and Y datasets

References

Md Ashad Alam, Kenji Fukumizu and Yu-Ping Wang (2018), Influence Function and Robust Variant of Kernel Canonical Correlation Analysis, Neurocomputing, Vol. 304 (2018) 12-29.

M. Romanazzi (1992), Influence in canonical correlation analysis, Psychometrika vol 57(2) (1992) 237-259.

See Also

See also as rkcca snpfmridata, ifrkcca

Examples

# NOT RUN {
##Dummy data:

X <- matrix(rnorm(2000),200); Y <- matrix(rnorm(2000),200)

rkcco(X,Y, "Huber","rbfdot", 1e-05)
# }