rkcco: Robust kernel cross-covariance opetator

In RKUM: Robust Kernel Unsupervised Methods

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

Author(s)

Md Ashad Alam <[e-mail]>

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

##Dummy data:

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

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

RKUM documentation built on June 22, 2022, 9:06 a.m.